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m demknn1.m

%DEMKNN1 Demonstrate nearest neighbour classifier. % % Description % The problem consists of data in a two-dimensional space. The data is % drawn from three spherical Gaussian distributions with
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m~ demhmc1.m~

%DEMHMC1 Demonstrate Hybrid Monte Carlo sampling on mixture of two Gaussians. % % Description % The problem consists of generating data from a mixture of two % Gaussians in two dimensions using a
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m demglm1.m

%DEMGLM1 Demonstrate simple classification using a generalized linear model. % % Description % The problem consists of a two dimensional input matrix DATA and a % vector of classifications T. Th
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m demglm2.m

%DEMGLM2 Demonstrate simple classification using a generalized linear model. % % Description % The problem consists of a two dimensional input matrix DATA and a % vector of classifications T. Th
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c parser_fl.c

/* parse (...) : 1. Reads maximal flow problem in extended DIMACS format. 2. Prepares internal data representation. types: 'arc' and 'node' must be predefined typ
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pas wwspeedbutton.pas

{ // // Components : TwwSpeedButton // // Copyright (c) 1998-2001 by Woll2Woll Software // // 10/7/98 - Fix paint problem with speedbutton when it is displayed the first time // // Enhancement
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m afqrrls2.m

%AFQRRLS2 Problem 1.1.1.2.10 % % 'ifile.mat' - input file containing: % I - members of ensemble % K - iterations % a1 - coefficient of input AR process % sigmax - standar
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m fqrrls3.m

%FQRRLS3T Problem 3.4 % % 'ifile.mat' - input file containing: % I - members of ensemble % K - iterations % s - deterministic part of signal to predict % sigman - standar
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m fqrrls2.m

%FQRRLS2 Problem 1.1.1.2.9 % % 'ifile.mat' - input file containing: % I - members of ensemble % K - iterations % a1 - coefficient of input AR process % sigmax - standard
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m eflrls2.m

%EFLRLS2 Problem 1.1.1.2.6 % % 'ifile.mat' - input file containing: % I - members of ensemble % K - iterations % a1 - coefficient of input AR process % sigmax - standard