k-meansy算法源代碼。This directory contains code implementing the K-means algorithm. Source code
may be found in KMEANS.CPP. Sample data isfound in KM2.DAT. The KMEANS
program accepts input consisting of vectors and calculates the given
number of cluster centers using the K-means algorithm. Output is
directed to the screen.
In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar -xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
This a very simple baseband simulator for SC-FDMA system. This simulator is part of the upcoming book “Single Carrier FDMA: A New Air Interface for Long Term Evolution” (Wiley, Nov. 2008) which I co-authored with professor David J. Goodman at Polytechnic University.
The purpose of this simulator is to give some concrete idea of how SC-FDMA system works. It does lack many realistic and sophisticated features such as channel coding, time-varying fading channel model, soft decision decoding, etc. Regardless, I am hoping that it will help you understand SC-FDMA which is a fairly new development in 3GPP LTE.
Recent advances in experimental methods have resulted in the generation
of enormous volumes of data across the life sciences. Hence clustering and
classification techniques that were once predominantly the domain of ecologists
are now being used more widely. This book provides an overview of these
important data analysis methods, from long-established statistical methods
to more recent machine learning techniques. It aims to provide a framework
that will enable the reader to recognise the assumptions and constraints that
are implicit in all such techniques. Important generic issues are discussed first
and then the major families of algorithms are described. Throughout the focus
is on explanation and understanding and readers are directed to other resources
that provide additional mathematical rigour when it is required. Examples
taken from across the whole of biology, including bioinformatics, are provided
throughout the book to illustrate the key concepts and each technique’s
potential.
This document contains official rules of the 3D soccer simulation competition
at RoboCup 2006. While we will try to cover all cases, if unexpected
events do occur, the rule committee will seek input from the
participants and then make a decision. However, once the committee has
made a decision, that decision is final and will not be open to further
discussion
個體商品銷售管理及分析系統
—進貨處理子系統本文在分析商品進貨、營銷、存儲系統及其特點的基礎上，提出將DSS技術運用到市場營銷決策上，開發MDSS（Marketing Decision Support System）、CRM、DSM的建議，并就MDSS的總體設計思想及研究方向作簡要介紹，以明確系統的基本組成與結構
aiParts is a set of C++ classes that can be used to develop artificial intelligence for multi-decision problems. It includes classes that implement the High-Hope technique and some sample programs.
c pgm to find redundant paths in a graph.Many fault-tolerant network algorithms rely on an underlying assumption that there are possibly distinct network paths between a source-destination pair. Given a directed graph as input, write a program that uses depth-first search to determine all such paths. Note that, these paths are not vertex-disjoint i.e., the vertices may repeat but they are all edge-disjoint i.e., no two paths have the same edges. The input is the adjacency matrix of a directed acyclic graph and a pair(s) of source and destination vertices and the output should be the number of such disjoint paths and the paths themselves on separate lines. In case of multiple paths the output should be in order of paths with minimum vertices first. In case of tie the vertex number should be taken in consideration for ordering.
