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Mean

  • % EM algorithm for k multidimensional Gaussian mixture estimation % % Inputs: % X(n,d) - input da

    % 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

  • % decode with soft-input viterbi algorithm 硬判決 % //k=4,r=1/2 %輸入數據為軟信息

    % 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

  • The package includes 3 Matlab-interfaces to the c-code: 1. inference.m An interface to the full

    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

  • ClustanGraphics聚類分析工具。提供了11種聚類算法。 Single Linkage (or Minimum Method, Nearest Neighbor) Complete Li

    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 Mean

    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

  • PRINCIPLE: The UVE algorithm detects and eliminates from a PLS model (including from 1 to A componen

    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

  • neural network utility is a Neural Networks library for the C++ Programmer. It is entirely object o

    neural network utility is a Neural Networks library for the C++ Programmer. It is entirely object oriented and focuses on reducing tedious and confusing problems of programming neural networks. By this I Mean that network layers are easily defined. An entire multi-layer network can be created in a few lines, and trained with two functions. Layers can be connected to one another easily and painlessly.

    標簽: Programmer Networks entirely network

    上傳時間: 2013-12-24

    上傳用戶:liuchee

  • 韓國Yunjin Lee的博士論文

    韓國Yunjin Lee的博士論文,包含了她讀博期間的主要研究成果,包括Mesh Scissoring: Contour-Based Segmentation,Mesh Parameterization Using Virtual Boundaries,Geometric Snakes for 3D Meshes,Mean Shift for 3D Meshes

    標簽: Yunjin Lee 韓國 論文

    上傳時間: 2013-12-12

    上傳用戶:netwolf

  • GPS 接收程序 DEMO。 HsGpsDll Library 1.1 A GPS Control/Component for C/C++ HsGpsDll is a Windows Dyn

    GPS 接收程序 DEMO。 HsGpsDll Library 1.1 A GPS Control/Component for C/C++ HsGpsDll is a Windows Dynamic Link Library which provides access to any NMEA-183 compliant GPS receiver via a serial communications port. HsGpsDll is designed for use from Visual C, Visual Basic or other languages, capable of calling DLL functions. HsGpsDll allows a user application to read from a GPS device the current GPS position fix, velocity over ground (speed in kilometers per hour), plus number of of sattelites in view, current altitude (against Mean sea level) and UTC date and time

    標簽: HsGpsDll GPS Component Control

    上傳時間: 2014-07-17

    上傳用戶:thuyenvinh

  • 1. 統計工資 設計要求: (1) 使用結構數組設計一個公司職員的數據結構

    1. 統計工資 設計要求: (1) 使用結構數組設計一個公司職員的數據結構,使用下述的結構定義: struct employee{ int age char name[15] double salary } (2) 在主函數里構造一個數組company,用來存放職工信息。 (3) 設計update函數,用來對company中指定職員的信息進行更改。要求先按照name查詢到相應的職員,然后修改并保存。 (4) 設計一個read函數,用來向company中錄入職員信息,并顯示結果。 (5) 編寫Mean函數求平均工資。 應該能對全體職工或大于某一年齡的職工工資求和并計算平均值。 (6) 編寫total函數對工資求和。 應該能對全體職工或某一年齡段職工的工資求和。

    標簽: 數組 數據結構

    上傳時間: 2014-01-23

    上傳用戶:colinal

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