Gaussian Integral

Gaussian Output Tools is a package of simple scripts to extract data from Gaussian output files.

This function returns coefficients of Gaussian lowpass filter.Advantages of Gaussian filter: no ringing or overshoot in time domain.Diasadvantage: slow rolloff in frequency domain.Pass SR=sampling rate, fco=cutoff freq, both in Hz, to the function.Coefficients for FIR filter of length L (L always...

The GMM returns the cluster centroid and cluster variances for a family of points if the number of clusters are predefined. The centroid and variance can then be passed to a Gaussian pdf to compute the similarity of a input query point with reference to given cluster.


Platforms: Matlab

Toolbox for log-spectral magnitude MMSE estimators under super-Gaussian densities.The toolbox can also be downloaded from: http://ict.ewi.tudelft.nl/~richard/log_spe...per_gaussV1.rarThe matlab files enclosed in this toolbox can be used to tabulate gain functions for log-spectral magnitude MMSE...


Platforms: Matlab

The Expectation-Maximization algorithm (EM) is widely used to find the parameters of a mixture of Gaussian probability density functions (pdfs) or briefly Gaussian components that fits the sample measurement vectors in maximum likelihood sense [1]. In our work, the expectation-maximization (EM)...


Platforms: Matlab

Fit a multivariate gaussian mixture by a cross-entropy method. Cross-Entropy is a powerfull tool to achieve stochastic multi-extremum optimization.Please visit http://iew3.technion.ac.il/CE/ for more informationsi) Please compile mex-files by the mexme_ce_gauss.m (if compiler is not setup, run...


Platforms: Matlab

GMM-GMR is a set of Matlab functions to train a Gaussian Mixture Model (GMM) and retrieve generalized data through Gaussian Mixture Regression (GMR). It allows to encode efficiently any dataset in Gaussian Mixture Model (GMM) through the use of an Expectation-Maximization (EM) iterative learning...


Platforms: Matlab

Free Split and Merge Expectation-Maximization algorithm for Multivariate Gaussian Mixtures. This algorithm is suitable to estimate mixture parameters and the number of conpounds Usage ------- [logl , M , S , P] = fsmem_mvgm(Z , [M0] , [S0] , [P0] , [option]); Inputs ------- Z Measurements (d x N)...


Platforms: Matlab

Generate Gaussian or Laplacian pyramids, or reconstruct an image from a pyramid. Contains a demo script doing image blending using pyramids. The function is more convenient to use than the Matlab function impyramid.


Platforms: Matlab

This code can be used to solve a set of linear equations using Gaussian elimination with partial pivoting. Note that the Augmented matrix rows are not directly switches. Instead a buffer vector is keeping track of the switches made. The final solution is determined using backward substitution.


Platforms: Matlab

em_ghmm : Expectation-Maximization algorithm for a HMM with Multivariate Gaussian measurement Usage ------- [logl , PI , A , M , S] = em_ghmm(Z , PI0 , A0 , M0 , S0 , [options]); Inputs ------- Z Measurements (m x K x n1 x ... x nl) PI0 Initial probabilities (d x 1) : Pr(x_1 = i) , i=1,...,d. PI0...


Platforms: Matlab

not getting results for 4 level/nlevel gaussian pyramid please try for improvement but gray levels are identified for successive levels and code is working wellunable to watch images for intermediate levels


Platforms: Matlab

This is a parallel implementation of the Expectation Maximization algorithm for multidimensional Gaussian Mixture Models, designed to run on NVidia graphics cards supportingCUDA. On my machine, it provides up to 170x performance increases (16 dims, 16 clusters, 1000000 data points).See the report...


Platforms: Matlab

We present here a program Fie that solves numerically Fredholm integral equations of the second kind on an interval [a,b] to a specified, modest accuracy. Test programs are also included for the various cases. The program TestAll, with no input parameters, can be used to illustrate the various...


Platforms: Matlab

Implementation details of (i)-(iii) can be found in [1].The fourth function (gmm2sv.m) is to concatenate the means (i.e. centers) of GMM. The concatenated mean of adapted GMM is known as GMM supervector (GSV) and it is used in GMM-SVM based speaker recognition system. Details of GMM-SVM based...


Platforms: Matlab

The code is explained in the article: Okunev, Pavel, "Fast Computation of the Economic Capital, the Value at Risk and the Greeks of a Loan Portfolio in the Gaussian Factor Model" (July 1, 2005). http://ssrn.com/abstract=758505 This implements one factor Gaussian model. [pd]=pdgs(L,w,p,LL,N) L =...


Platforms: Matlab

A fast generator of gaussian mixture samples with a general ND dimensional calling syntax.Permit to sample from a simple multivariate process to several gaussian mixture in a easy way.Please run mexme_sample_mvgm.m to compile on your own platform. (Be sure to mex -setup is done before)


Platforms: Matlab

HYPERGEOMETRIC2F1 Computes a hypergeometric function using the series expansion:f(a,b;c;x)=1 + [ab/1!c]x + [a(a+1)b(b+1)/2!c(c+1)]x^2 +[a(a+1)(a+2)b(b+1)(b+2)/3!c(c+1)(c+2)]x^3 + ...This function solves the Gaussian Hypergeometric Differential Equation:x(1-x)y'' + {c-(a+b+1)x}y' - aby = 0


Platforms: Matlab

The algorithm takes in a 1-D signal and finds the Gaussian derivative of it with the given kernel size and sigma, to provide the zero crossings. The zero crossings are then analyzed to give peaks and valleys. In order to remove very closely situated peaks and valleys, a width (provided by the...


Platforms: Matlab

MVG is a multivariate Gaussian (normal) random number generator. A user can generate a vector from the multivariate normal distribution of any dimension by specifying a mean vector and symmetric positive-definite covariance matrix. A linear transformation based on the Cholesky decomposition of...


Platforms: Matlab