The package includes 3 Matlab-interfaces to the c-code: 1. inference.m An interface to the full inference package, includes several methods for approximate inference: Loopy Belief Propagation, Generalized Belief Propagation, Mean-Field approximation, and 4 monte-carlo sampling methods (Metropolis, Gibbs, Wolff, Swendsen-Wang). Use "help inference" from Matlab to see all options for usage. 2. gbp_preprocess.m and gbp.m These 2 interfaces split Generalized Belief Propagation into the pre-process stage (gbp_preprocess.m) and the inference stage (gbp.m), so the user may use only one of them, or changing some parameters in between. Use "help gbp_preprocess" and "help gbp" from Matlab. 3. simulatedAnnealing.m An interface to the simulated-annealing c-code. This code uses Metropolis sampling method, the same one used for inference. Use "help simulatedAnnealing" from Matlab.
標簽： Matlab-interfaces inference interface the
上傳時間： 2016-08-27
上傳用戶：gxrui1991
this directory contains the following: * The acdc algorithm for finding the approximate general (non-orthogonal) joint diagonalizer (in the direct Least Squares sense) of a set of Hermitian matrices. [acdc.m] * The acdc algorithm for finding the same for a set of Symmetric matrices. [acdc_sym.m](note that for real-valued matrices the Hermitian and Symmetric cases are similar however, in such cases the Hermitian version [acdc.m], rather than the Symmetric version[acdc_sym] is preferable. * A function that finds an initial guess for acdc by applying hard-whitening followed by Cardoso s orthogonal joint diagonalizer. Note that acdc may also be called without an initial guess, in which case the initial guess is set by default to the identity matrix. The m-file includes the joint_diag function (by Cardoso) for performing the orthogonal part. [init4acdc.m]
標簽： approximate directory algorithm the
上傳時間： 2014-01-17
上傳用戶：hanli8870
The toolbox solves a variety of approximate modeling problems for linear static models. The model can be parameterized in kernel, image, or input/output form and the approximation criterion, called misfit, is a weighted norm between the given data and data that is consistent with the model. There are three main classes of functions in the toolbox: transformation functions, misfit computation functions, and approximation functions. The approximation functions derive an approximate model from data, the misfit computation functions are used for validation and comparison of models, and the transformation functions are used for deriving one model representation from another. KEYWORDS: Total least squares, generalized total least squares, software implementation.
標簽： approximate The modeling problems
上傳時間： 2013-12-20
上傳用戶：15071087253
ClustanGraphics聚類分析工具。提供了11種聚類算法。 Single Linkage (or Minimum Method, Nearest Neighbor) Complete Linkage (or Maximum Method, Furthest Neighbor) Average Linkage (UPGMA) Weighted Average Linkage (WPGMA) Mean Proximity Centroid (UPGMC) Median (WPGMC) Increase in Sum of Squares (Ward s Method) Sum of Squares Flexible (ß space distortion parameter) Density (or k-linkage, density-seeking mode analysis)
標簽： ClustanGraphics Complete Neighbor Linkage
上傳時間： 2014-01-02
上傳用戶：003030
A Module-based Wireless Node (MW-Node) is a Node with wireless and mobile capabilities added by means of modules. It is not a new node object derived from Node. Rather it is a new layout of mostly existing components. Rationale for this new design has been presented in [1]. The MW-Node provides a flexible support for wireless and mobile networking and in particular: support for multiple interfaces/multiple channels, and a common basis for the implementation of wireless routing protocols.
標簽： Node Module-based capabilities Wireless
上傳時間： 2013-12-26
上傳用戶：大三三
Jvm 規范說明。The Java Virtual Machine was designed to support the Java programming language. Some concepts and vocabulary from the Java language are thus necessary to understand the virtual machine. This chapter gives enough of an overview of Java to support the discussion of the Java Virtual Machine to follow. Its material has been condensed from The Java Language Specification, by James Gosling, Bill Joy, and Guy Steele. For a complete discussion of the Java language, or for details and examples of the material in this chapter, refer to that book. Readers familiar with that book may wish to skip this chapter. Readers familiar with Java, but not with The Java Language Specification, should at least skim this chapter for the terminology it introduces.
標簽： Java programming designed language
上傳時間： 2013-12-19
上傳用戶：wangyi39
The inverse of the gradient function. I ve provided versions that work on 1-d vectors, or 2-d or 3-d arrays. In the 1-d case I offer 5 different methods, from cumtrapz, and an integrated cubic spline, plus several finite difference methods. In higher dimensions, only a finite difference/linear algebra solution is provided, but it is fully vectorized and fully sparse in its approach. In 2-d and 3-d, if the gradients are inconsistent, then a least squares solution is generated
標簽： gradient function provided versions
上傳時間： 2016-11-07
上傳用戶：秦莞爾w
After the successful global introduction during the past decade of the second generation (2G) digital mobile communications systems, it seems that the third generation (3G) Universal Mobile Communication System (UMTS) has finally taken off, at least in some regions. The plethora of new services that are expected to be offered by this system requires the development of new paradigms in the way scarce radio resources should be managed. The Quality of Service (QoS) concept, which introduces in a natural way the service differentiation and the possibility of adapting the resource consumption to the specific service requirements, will open the door for the provision of advanced wireless services to the mass market.
標簽： the introduction successful generation
上傳時間： 2013-12-30
上傳用戶：qq21508895
PRINCIPLE: The UVE algorithm detects and eliminates from a PLS model (including from 1 to A components) those variables that do not carry any relevant information to model Y. The criterion used to trace the un-informative variables is the reliability of the regression coefficients: c_j=mean(b_j)/std(b_j), obtained by jackknifing. The cutoff level, below which c_j is considered to be too small, indicating that the variable j should be removed, is estimated using a matrix of random variables.The predictive power of PLS models built on the retained variables only is evaluated over all 1-a dimensions =(yielding RMSECVnew).
標簽： from eliminates PRINCIPLE algorithm
上傳時間： 2016-11-27
上傳用戶：凌云御清風
ST uPSD32XX I2C This example demo code is provided as is and has no warranty, implied or otherwise. You are free to use/modify any of the provided code at your own risk in your applications with the expressed limitation of liability (see below) so long as your product using the code contains at least one uPSD products (device)
標簽： otherwise provided warranty example
上傳時間： 2013-12-07
上傳用戶：標點符號
