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www.eeworm.com/read/140851/13059096

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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txt alg112.txt

> restart; > # NONLINEAR SHOOTING ALGORITHM 11.2 > # > # To approximate the solution of the nonlinear boundary-value problem > # > # Y'' = F(X,Y,Y'), A
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txt alg055.txt

> restart; > # ADAMS VARIABLE STEP-SIZE PREDICTOR-CORRECTOR ALGORITHM 5.5 > # > # To approximate the solution of the initial value problem > # y' = f( t, y ), a
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txt alg112.txt

> restart; > # NONLINEAR SHOOTING ALGORITHM 11.2 > # > # To approximate the solution of the nonlinear boundary-value problem > # > # Y'' = F(X,Y,Y'), A
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txt alg055.txt

> restart; > # ADAMS VARIABLE STEP-SIZE PREDICTOR-CORRECTOR ALGORITHM 5.5 > # > # To approximate the solution of the initial value problem > # y' = f( t, y ), a
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c alg051.c

/* * EULER'S ALGORITHM 5.1 * * TO APPROXIMATE THE SOLUTION OF THE INITIAL VALUE PROBLEM: * Y' = F(T,Y), A
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c alg055.c

/* * ADAMS VARIABLE STEP-SIZE PREDICTOR-CORRECTOR ALGORITHM 5.5 * * To approximate the solution of the initial value problem * * y' = f( t, y ), a