数值分析三角直接分解法

利用dijkstra算法编写的公交线路的最小换乘问题的算法-Dijkstra algorithm to use the bus lines to prepare the smallest change Algorithm

分数阶傅立叶变换.rar-fractional Fourier transform. Rar

应用背景本代码主要实现搜索引擎的几个评估准则，包括NDCG@K、DCG@K、P@K、RR@K、ERR@K这五类评估准则的实现，这代码主要是根据RankLib中的评估准则代码以scala的形似展现出来，相对于RankLib中的 评估代码而言，本代码短小，可读性强，可移植性大，关键技术本文使用 的scala实现，对NDCG@K、DCG@K、P@K、RR@K、ERR@K这五类评估准则的实现，跟RankLib中的评估公式保持一致

详细表述了分数阶傅立叶变换的离散实现，将现有的算法进行比较。-In this note we make a critical comparison of some matlab programs for the digital computation of the fractional Fourier transform that are freely available and we describe our own implementation that lters the best out of the existing ones. Two types of transforms are considered: First the fast approximate fractional Fourier transform algorithm for which two algorithms are available. The method is described in H.M. Ozaktas, M.A. Kutay, and G. Bozdagi. Digital computation of the fractional Fourier transform. IEEE Trans. Signal Process., 44:2141{2150, 1996. There are two implementations: one is written by A.M. Kutay the other is part of package written by J. O Neill. Secondly the discrete fractional Fourier transform algorithm described in the master thesis C . Candan. The discrete fractional Fourier transform, Bilkent Univ., 1998 and an algorithm described by S.C. Pei, M.H. Yeh, and C.C Tseng: Digital fractional Fourier transform base

蚁群算法运用在聚类中，并且用C语言实现，并且会有界面展示蚁群聚类的过程。简单形象易懂

应用背景package Kmeans; import java.io.BufferedReader; import java.io.FileReader; import java.util.ArrayList; public class KMeans { private static String fileName = "E:java练习Arithmeticjulei1.txt"; // 文件地址 private static int numTotal; // 聚类总数 private static int K; // 聚类的簇数关键技术典型的聚类算法，应用广发，学习简单，java技术的使用

用VB实现线性方程组的求解。包括：高斯消元法，LU分解法追赶法，迭代法，奇异值分解，乔累斯基分解法等12种数值方法。-VB of linear equations to solve. Including : Gaussian Elimination Act, the law catch up with the LU decomposition, iterative method, singular value decomposition, Joe Cholesky decomposition 12 species of numerical methods.

经典算法之:连续邮资问题,全排列问题,有限期任务安排,整数划分问题,装载问题-classic algorithms : for the postage issue, all issues with a task deadline, integer division, loading problems

gis sample图形排版，二维图形处理与GIS图形处理.

应用背景Implement k-means clustering on a training data set  to predict classification values of a test data set.关键技术The experiment intended to classify test data values using the data   mining strategy known as k-means clustering.   The 6 variables in the training data are used to plot the data and   centroids are placed to locate clumps of the 255, classified training   data. Once located, a classification value can be assigned to the 100,   test data.