The IA-32 Intel Architecture Software Developer’s Manual, Volume 2: Instruction Set Reference (Order Number 245471) is part of a three-volume set that describes the architecture and programming environment of all IA-32 Intel® Architecture processors. the IA-32 Intel Architecture Software Developer’s Manual, Volume 2, describes the instructions set of the processor and the opcode structure. These two volumes are aimed at application programmers who are writing programs to run under existing operating systems or executives.
标签： Architecture Instruction Developer Reference
上传时间： 2013-12-15
上传用户：xsnjzljj
伪随机数生成器，Implementation of the Quasi-Random Number generator currently hardwired to no more than 52 dimensions
上传时间： 2013-12-20
上传用户：teddysha
1) Write a function reverse(A) which takes a matrix A of arbitrary dimensions as input and returns a matrix B consisting of the columns of A in reverse order. Thus for example, if A = 1 2 3 then B = 3 2 1 4 5 6 6 5 4 7 8 9 9 8 7 Write a main program to call reverse(A) for the matrix A = magic(5). Print to the screen both A and reverse(A). 2) Write a program which accepts an input k from the keyboard, and which prints out the smallest fibonacci Number that is at least as large as k. The program should also print out its position in the fibonacci sequence. Here is a sample of input and output: Enter k>0: 100 144 is the smallest fibonacci Number greater than or equal to 100. It is the 12th fibonacci Number.
标签： dimensions arbitrary function reverse
上传时间： 2016-04-16
上传用户：waitingfy
This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (Number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
标签： sequential reversible algorithm nstrates
上传时间： 2014-01-18
上传用户：康郎
This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (Number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar -xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
标签： reversible algorithm the nstrates
上传时间： 2014-01-08
上传用户：cuibaigao
北京大学ACM比赛题目 In 1742, Christian Goldbach, a German amateur mathematician, sent a letter to Leonhard Euler in which he made the following conjecture: Every even Number greater than 4 can be written as the sum of two odd prime Numbers. For example: 8 = 3 + 5. Both 3 and 5 are odd prime Numbers. 20 = 3 + 17 = 7 + 13. 42 = 5 + 37 = 11 + 31 = 13 + 29 = 19 + 23. Today it is still unproven whether the conjecture is right. (Oh wait, I have the proof of course, but it is too long to write it on the margin of this page.) Anyway, your task is now to verify Goldbach s conjecture for all even Numbers less than a million.
标签： mathematician Christian Goldbach Leonhard
上传时间： 2016-04-22
上传用户：wangchong
北京大学ACM比赛题目 Consider an infinite full binary search tree (see the figure below), the Numbers in the nodes are 1, 2, 3, .... In a subtree whose root node is X, we can get the minimum Number in this subtree by repeating going down the left node until the last level, and we can also find the maximum Number by going down the right node. Now you are given some queries as "What are the minimum and maximum Numbers in the subtree whose root node is X?" Please try to find answers for there queries.
标签： the Consider infinite Numbers
上传时间： 2013-12-16
上传用户：日光微澜
This little Program allows you to send commands to the CardReader (CT-BCS) or to the Card itself. First you will be ask to what Port the Reader is connected (0=COM1, 1=COM2). Then the Class-Byte (CLA), Instruction-Byte (INS), Parameter 1 (P1), Parameter 2 (P2). If wou don愒 want to send Parameter 3 (P3) press Ctrl-d (^d). If you enter a Number then you have to the Bytes of the Datafield. After the last Byte of the Datafield is entered the whole APDU is send the replay is displayed. After that you can send the next APDU.
标签： CardReader the commands to
上传时间： 2016-04-23
上传用户：nanxia
PlotSphereIntensity(azimuth, elevation) PlotSphereIntensity(azimuth, elevation, intensity) h = PlotSphereIntensity(...) Plots the intensity (as color) of a Number of points on a unit sphere. Input: azimuth (phi), in degrees elevation (theta), in degrees intensity (optional, if not provided, a green sphere is produced) All inputs must be vectors or matrices of the same size. Data does not have to be evenly spaced. When there aren t enough points to draw a smooth sphere, additional points (with color) are interpolated. Output: h - a handle to the patch object The axes are also plotted: positive x axis is red positive y axis is green positive z axis is blue
标签： PlotSphereIntensity elevation azimuth intensity
上传时间： 2014-01-15
上传用户：ruan2570406
Creates a Gaussian mixture model with specified architecture.MIX = GMM(DIM, NCENTRES, COVARTYPE) takes the dimension of the space DIM, the Number of centres in the mixture model and the type of the mixture model, and returns a data structure MIX.
标签： architecture COVARTYPE specified Gaussian
上传时间： 2016-04-28
上传用户：dyctj
