Ultra math wizard gold in c

The following C project contains the C source code and C examples used for ultra math wizard gold. Hey guys , please vote for this one. This program can create any math c code using a few details from user. Create many physics and maths application in matter of seconds. The Gold Wizard is here.

Mrmr feature selection (using mutual information computation) in matlab

The following Matlab project contains the source code and Matlab examples used for mrmr feature selection (using mutual information computation). This package is the mRMR (minimum-redundancy maximum-relevancy) feature selection method in (Peng et al, 2005 and Ding & Peng, 2005, 2003), whose better performance over the conventional top-ranking method has been demonstrated on a number of data sets in recent publications.

Minimum redundancy maximum relevance feature selection in matlab

The following Matlab project contains the source code and Matlab examples used for minimum redundancy maximum relevance feature selection. Two source code files of the mRMR (minimum-redundancy maximum-relevancy) feature selection method in (Peng et al, 2005 and Ding & Peng, 2005, 2003), whose better performance over the conventional top-ranking method has been demonstrated on a number of data sets in recent publications.

Generalized principal component pursuit in matlab

The following Matlab project contains the source code and Matlab examples used for generalized principal component pursuit. This is a generalized version of Principal Component Pursuit (PCP) where the sparsity is assumed in a transform domain and not in measurement domain.

Improvd downward branch and bound algorithm for regression variable selection in matlab

The following Matlab project contains the source code and Matlab examples used for improvd downward branch and bound algorithm for regression variable selection. Subset (feature) selection for least squares regression is a common problem, which is combinartorial, hence is computationally NP hard.

Robust multidimensional scaling (mds) using ecdfs in matlab

The following Matlab project contains the source code and Matlab examples used for robust multidimensional scaling (mds) using ecdfs. Take m points in R^n and transform those points to R^p using random vectors such that the ECDF of L1 distances are preserved.

Low rank multivariate autoregressive model for dimensionality reduction in matlab

The following Matlab project contains the source code and Matlab examples used for low rank multivariate autoregressive model for dimensionality reduction. Despite the fact that they do not consider the temporal nature of data, classic dimensionality reduction techniques, such as PCA, are widely applied to time series data.

Recovery of low rank and sparse matrix in matlab

The following Matlab project contains the source code and Matlab examples used for recovery of low rank and sparse matrix. This code solves the problem of recovering a low rank and sparse(in transform domain)matrix from its lower dimensional projections Minimize (lambda1)||X||* + (lambda2)||Dx||_1 + 1/2 || A(X) - y ||_2^2 Formulated as an unconstarined nuclear norm and L1 minimization problem using Split bregman algorithm, formulation for the problem is as follows

Efficient multidimensional scaling (mds) in matlab

The following Matlab project contains the source code and Matlab examples used for efficient multidimensional scaling (mds). Quan Wang, Kim L. Boyer. Feature Learning by Multidimensional Scaling and its Applications in Object Recognition. 2013 26th SIBGRAPI Conference on Graphics, Patterns and Images (Sibgrapi). IEEE, 2013. Project wiki: https://sites.google.com/site/mdsfeature/ C++ implementation of MDS: https://sites.google.com/site/simpmatrix/

Feature selection using matlab

The following Matlab project contains the source code and Matlab examples used for feature selection using matlab. The DEMO includes 5 feature selection algorithms: • Sequential Forward Selection (SFS) • Sequential Floating Forward Selection (SFFS) • Sequential Backward Selection (SBS) • Sequential Floating Backward Selection (SFBS) • ReliefF Two CCR estimation methods: • Cross-validation • Resubstitution After selecting the best feature subset, the classifier obtained can be used for classifying any pattern.

Multiple correspondence analysis based on the burt matrix. in matlab

The following Matlab project contains the source code and Matlab examples used for multiple correspondence analysis based on the burt matrix.. Statistics fundamentals of the Correspondence Analysis (CA) is presented in the CORRAN and MCORRAN1 m-files you can find in this FEX author''s page.

Multiple correspondence analysis based on the indicator matrix. in matlab

The following Matlab project contains the source code and Matlab examples used for multiple correspondence analysis based on the indicator matrix.. Statistic fundamentals of he Correspondence Analysis (CA) is presented in the CORRAN m-file you can find in this FEX author''s page.

Correspondence analysis. in matlab

The following Matlab project contains the source code and Matlab examples used for correspondence analysis. . Correspondence Analysis (CA) is a special case of Canonical Correlation Analysis (CCA), where one set of entries (categories rather than variables) is related to another set.

Rafisher2cda in matlab

The following Matlab project contains the source code and Matlab examples used for rafisher2cda. Canonical discriminant analysis is a dimension-reduction technique related to principal component analysis and canonical correlation called canonical discriminant analysis.

Factor analysis by the principal components method without data. in matlab

The following Matlab project contains the source code and Matlab examples used for factor analysis by the principal components method without data. . This m-file deals with the principal component solution of the factor model thru the R-correlation matrix (without the matrix of data; one can also input a covariance matrix), the latent root criterion, and uses the varimax factor rotation.

Expectation-maximization principal component analysis in matlab

The following Matlab project contains the source code and Matlab examples used for expectation-maximization principal component analysis . EMPCA calculates principal components using an expectation maximization algorithm to find each component in the residual matrix after substracting the previously converged principal components.

Factor analysis by the principal components method. in matlab

The following Matlab project contains the source code and Matlab examples used for factor analysis by the principal components method. . This m-file deals with the principal component solution of the factor model thru the complete data matrix, the latent root criterion, and uses the varimax factor rotation.

Directional discrete cosine transform and principal component analysis based image fusion in matlab

The following Matlab project contains the source code and Matlab examples used for directional discrete cosine transform and principal component analysis based image fusion. Image fusion algorithm based on DDCT and PCA is demonstrated. The reference is: VPS Naidu, "Hybrid DDCT-PCA base multi sensor image fusion”, Journal of Optics, Vol. 43, No.1, pp.48-61, March 2014.

Multilinear principal component analysis (mpca) in matlab

The following Matlab project contains the source code and Matlab examples used for multilinear principal component analysis (mpca). Matlab source codes for Multilinear Principal Component Analysis (MPCA) %[Algorithms]% The matlab codes provided here implement two algorithms presented in the paper "MPCA_TNN08_rev2012.

Information theoretic feature selection in matlab

The following Matlab project contains the source code and Matlab examples used for information theoretic feature selection. Description: Code (Matlab/C++ Mex) for the following MI based feature selection approaches: - Maximum relevance (maxRel) - Minimum redundancy maximum relevance (MRMR) - Minimum redundancy (minRed) - Quadratic programming feature selection (QPFS) - Mutual information quotient (MIQ) - Maximum relevance minimum total redundancy (MRMTR) or extended MRMR (EMRMR) - Spectral relaxation global Conditional Mutual Information (SPEC_CMI)

Orthogonal linear regression in 3d space by using principal components analysis in matlab

The following Matlab project contains the source code and Matlab examples used for orthogonal linear regression in 3d space by using principal components analysis. Orthogonal Linear Regression in 3D-space by using Principal Components Analysis This is a wrapper function to some pieces of the code from the Statistics Toolbox demo titled "Fitting an Orthogonal Regression Using Principal Components Analysis" (http://www.

Shuffled complex evolution with pca (sp uci) method in matlab

The following Matlab project contains the source code and Matlab examples used for shuffled complex evolution with pca (sp uci) method. The shuffled complex evolution with principal components analysis–University of California at Irvine (SP-UCI) method is a global optimization algorithm designed for high-dimensional and complex problems.

Kl transform (karhunen–loève theorem) in matlab

The following Matlab project contains the source code and Matlab examples used for kl transform (karhunen–loève theorem). this is for the true beginners!! Sorry for not providing any comments inside the code....its too simple.!!

Kernel pca and pre image reconstruction in matlab

The following Matlab project contains the source code and Matlab examples used for kernel pca and pre image reconstruction. In this package, we implement standard PCA, kernel PCA, and pre-image reconstruction of Gaussian kernel PCA.

Principal component analysis (pca) in matlab

The following Matlab project contains the source code and Matlab examples used for principal component analysis (pca) in matlab. This is a demonstration of how one can use PCA to classify a 2D data set.

Principal component analysis in matlab

The following Matlab project contains the source code and Matlab examples used for principal component analysis. This is an efficient implementation of PCA, which use smaller dimension of the data matrix to compute the eigenvectors.

Pca (principial component analysis) in matlab

The following Matlab project contains the source code and Matlab examples used for pca (principial component analysis).  - Subtracting the mean of the data from the original dataset  - Finding the covariance matrix of the dataset  - Finding the eigenvector(s) associated with the greatest eigenvalue(s)  - Projecting the original dataset on the eigenvector(s)  - Use only a certain number of the eigenvector(s)  - Do back-project to the original basis vectors Implementation of http://www.

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