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Gaussian Mixture model
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Gaussian Mixture model is used in many fields to model a training set of data owing to certain similarities among them. My code estimates the parameters of a Gaussian mixture model by taking in the training set of data as input and giving back the mean, covariance and mixing ratios as the output. The code might be slow because of its sequential nature but it does perform well than the original matlab code gmdistribution.fit in case the amount of data is very large.
siniorcodeforge 1 0
1
Fuzzy Classifier
4.0
An extremely simple fuzzy classifier is identified based on the inconsistency analysis of labelled training data. The method was applied to the COIL challenge 2000 Direct Mail problem and resulted in 121 selected caravan policies within the first 800 selected customers. As this result is identical to the result of the winner of the competition, the presented method is an example for how the try the simplest first approach can be effective in real-life problems. This algorithm can be used for obtaining accurate but also interpretable fuzzy rule-based classifiers from labelled observation data. In the first step, the structure of the model is initialised based on the statistical analysis of the labelled data and straightforward data-mining tools like feature selection methods. After the feature selection step, the algorithm transforms the inconsistency analysis of the features into fuzzy sets. Finally, the classifier is optimised for accuracy by adaptin
siniorcodeforge 1 0
1
Data mining Toolbox
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Utility m-files to improve distance matrix usage and computation: - parallel distance matrix computation (pair_dist_par), - parallel distance computation using shared memory model capable of maintaining really large matrices (pair_dist_spmd) - requires sharedmatrix to be compiled and in path, - function for a comfortable referring to a vector form of a distance matrix (pseudo_squareform). The out=pair_dist_par(X,fun,parameters) works somewhat similar to standard pdist. It requires Parallel Computing Toolbox (PCT) but the difference is that distance function (fun) works in row-vs-row manner (instead row-vs-submatrix in pdist) and it is a handler to a distance function of a form d=fun(x,y,params), where: params is a variable list of parameters, x and y are single row vectors it is DIFFERENT to the pdist. One can provide or a handler to own distance function or a name of a built-in distance function, or (
siniorcodeforge 1 0
1
k-means intra cluster measure
no vote
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution
siniorcodeforge 1 0
1
Linear Discriminant Analysis
no vote
% LDA - MATLAB subroutine to perform linear discriminant analysis % Use: % W = LDA(Input,Target,Priors) % % W = discovered linear coefficients (first column is the constants) % Input = predictor data (variables in columns, observations in rows) % Target = target variable (class labels) % Priors = vector of prior probabilities (optional) % % Note: discriminant coefficients are stored in W in the order of unique(Target) % % Example: % % % Generate example data: 2 groups, of 10 and 15, respectively % X = [randn(10,2); randn(15,2) + 1.5]; Y = [zeros(10,1); ones(15,1)]; % % % Calculate linear discriminant coefficients % W = LDA(X,Y); % % % Calulcate linear scores for training data % L = [ones(25,1) X] * W'; % % % Calculate class prob
siniorcodeforge 0 0
1
Dimensionality Reduction
no vote
This Matlab toolbox implements 34 techniques for dimensionality reduction and metric learning.
These techniques are all available through the COMPUTE_MAPPING function or through the GUI.
The following techniques are available:
- Principal Component Analysis ('PCA')
- Linear Discriminant Analysis ('LDA')
- Multidimensional scaling ('MDS')
- Probabilistic PCA ('ProbPCA')
- Factor analysis ('FactorAnalysis')
- Sammon mapping ('Sammon')
siniorcodeforge 2016-08-23
siniorcodeforge 3 0
1
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