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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.
標簽： dimensions arbitrary function reverse
上傳時間： 2016-04-16
上傳用戶：waitingfy
The BeeStack Application Development Guide describes how to develop an application for BeeStack, including discussions on major considerations for commercial applications. This document is intended for software developers who write applications for BeeStack-based products using Freescale development tools. It is assumed the reader is a programmer with at least rudimentary skills in the C programming language and that the reader is already familiar with the edit/compile/debug process.
標簽： BeeStack Application Development application
上傳時間： 2016-04-17
上傳用戶：lijianyu172
KMEANS Trains a k means cluster model.CENTRES = KMEANS(CENTRES, DATA, OPTIONS) uses the batch K-means algorithm to set the centres of a cluster model. The matrix DATA represents the data which is being clustered, with each row corresponding to a vector. The sum of squares error function is used. The point at which a local minimum is achieved is returned as CENTRES.
標簽： CENTRES KMEANS OPTIONS cluster
上傳時間： 2014-01-07
上傳用戶：zhouli
aiNet application is a very powerful and a very simple tool for solving the problems which are usually solved with artificial neural networks (ANN). All possible tests we had run proved that the results obtained with aiNet are at least as good as the results obtained with some other ANNs. Let us state some of aiNet抯 features. (c) aiNet 1995-1997
標簽： very application powerful problems
上傳時間： 2014-01-16
上傳用戶：wang5829
This book is for the experience and not the same level of the design process so prepared by the staff, of course, the reader should at least be able to prepare a simple C language program. On the C language are learning readers, this book is any C language tutorial excellent supporting materials, to be able to answer all relevant questions.
標簽： the experience prepared process
上傳時間： 2013-12-20
上傳用戶：jcljkh
％ EM algorithm for k multidimensional Gaussian mixture estimation ％ ％ Inputs: ％ X(n,d) - input data, n=number of observations, d=dimension of variable ％ k - maximum number of Gaussian components allowed ％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none) ％ maxiter - maximum number of iteration allowed ([] for none) ％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) ％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) ％ ％ Ouputs: ％ W(1,k) - estimated weights of GM ％ M(d,k) - estimated mean vectors of GM ％ V(d,d,k) - estimated covariance matrices of GM ％ L - log likelihood of estimates ％
標簽： multidimensional estimation algorithm Gaussian
上傳時間： 2013-12-03
上傳用戶：我們的船長
GloptiPoly 3: moments, optimization and semidefinite programming. Gloptipoly 3 is intended to solve, or at least approximate, the Generalized Problem of Moments (GPM), an infinite-dimensional optimization problem which can be viewed as an extension of the classical problem of moments [8]. From a theoretical viewpoint, the GPM has developments and impact in various areas of mathematics such as algebra, Fourier analysis, functional analysis, operator theory, probability and statistics, to cite a few. In addition, and despite a rather simple and short formulation, the GPM has a large number of important applications in various fields such as optimization, probability, finance, control, signal processing, chemistry, cristallography, tomography, etc. For an account of various methodologies as well as some of potential applications, the interested reader is referred to [1, 2] and the nice collection of papers [5].
標簽： optimization semidefinite programming GloptiPoly
上傳時間： 2016-06-05
上傳用戶：lgnf
％ decode with soft-input viterbi algorithm 硬判決 ％ //k=4,r=1/2 ％輸入數據為軟信息，并且數據為均值為1的BPSK調制，如果均值為MEAN,那么62,63,103,104行應做相應修改
標簽： soft-input algorithm viterbi decode
上傳時間： 2014-10-28
上傳用戶：aig85
Numerical Computing with MATLAB （by Cleve Moler) is a textbook for an introductory course in numerical methods, Matlab, and technical computing. The emphasis is on in- formed use of mathematical software. We want you learn enough about the mathe- matical functions in Matlab that you will be able to use them correctly, appreciate their limitations, and modify them when necessary to suit your own needs. The topics include * introduction to Matlab, * linear equations, * interpolation, * zero and roots, * least squares, * quadrature, * ordinary di?erential equations, * random numbers, * Fourier analysis, * eigenvalues and singular values, * partial di?erential equations.
標簽： introductory Numerical Computing textbook
上傳時間： 2016-07-04
上傳用戶：思琦琦
Toolbox for Numerical Computing with MATLAB （by Cleve Moler). Numerical Computing with MATLAB （by Cleve Moler) is a textbook for an introductory course in numerical methods, Matlab, and technical computing. The emphasis is on in- formed use of mathematical software. We want you learn enough about the mathe- matical functions in Matlab that you will be able to use them correctly, appreciate their limitations, and modify them when necessary to suit your own needs. The topics include * introduction to Matlab, * linear equations, * interpolation, * zero and roots, * least squares, * quadrature, * ordinary di?erential equations, * random numbers, * Fourier analysis, * eigenvalues and singular values, * partial differential equations.
標簽： Numerical Computing MATLAB with
上傳時間： 2014-01-01
上傳用戶：guanliya
