List of Matlab M-Files Used in ECE/CS/ME 539

Last modification: December 18, 2006

These Matlab M-Files are written by Yu Hen Hu, and have been tested on Matlab V.7. You are welcomed to use them for education and research purposese. For commercial applications (including for-profit education services), please contact Prof. Hu at hu@engr.wisc.edu .

If you spot mistakes in these routines, I will be happy to hear from you to correct them. However, I am unable to provide free consultation solve your problem on hand. Nor will I help solving your homework problem unless you are taking my class during the present semester.

Learning

• bvv.m -- Bias versus variance trade-offs demonstration (use utility routines randomize.m and polyfit1d.m)
• lamexample.m -- Example of linear associative memory
• ghademo.m -- Principal component analysis using generalized Hebbian learning. (use ghafun.m)
• learner.m -- demonstration of error correcting learning (LMS) (use utility routines randomize.m)
• lmsdemo.m -- demonstration of LMS algorithm LMS learning
• perceptron.m -- perceptron learning algorithm (use utility routines sline.m, datasepf.m)

Back-propagation Multi-Layer Perceptron

• bp.m -- MLP backpropagation driver program for classification tasks. (use utility routines scale.m, and randomize.m.) Other mfiles needed:
• bpconfig.m -- Initial configurations of a MLP
• cvgtest.m -- Convergence test routine (updated 10/16/01).
• bpdisplay.m -- Display intermediate and final result of BP training
• bptest.m, bptestap.m -- Test classification (bptest.m) and approximation (bptestap.m) results
• rsample.m -- random sample K out of Kr rows from matrix x.
• actfun.m, actfunp.m -- Compute activation function and their derivatives.
• partunef.m -- partition the training data samples x into a training set and a tuning set according to a user specified percentage ratio
• fsplit.m -- Random partition a matrix x row-wise according to ratio:1
• mlpdemo1.m -- demonstration of feedforward MLP's output as function of its two input
• mlp2.m -- a specialized 2-layer feedforward MLP network, called by mlpdemo1.m

Pattern Classification

• knndemo.m, knn.m: knearest neighbor classifier
• mldemo.m: Maximum likelihood classifier with Gaussion mixture distribution. Call mltrainnew.m, mltestnew.m, mlgmm.m, csm.m, use utility routines datagen.m, and netlab routines: gmm.zip (updated: 10/2003)

Support Vector Machines

• svmdemo.m -- demonstration of using libsvm to solve a classification problem using svm. updated: November 23, 2005 with LibSVM v.2.8.1 matlab interface. Download two DLL files (for window system only) svmtrain.dll, and svmpredict.dll. The utility routines datasepf.m, and datagen.m have also been updated.

Clustering

• learncl.m -- Competitive Learning demonstration (use utility routines datagen1.m). and
  learncl1.m -- Annimation version of learncl.m
• clusterdemo.m -- Kmean clustering algorithm demonstration. Call kmeansf.m, and utility routines datagen.m
• kmeansf.m -- A function implementing the kmeans clustering algorithm
• cinit.m -- initilize clustering algorithm, call c1d.m, 1D clustering algorithm
• somdemo.m -- Self Organizing Map demonstration program as well as utility routines datagen1.m and randomize.m
• kerneldemo.m - demonstration of Parzen window. Call kernel1d.m
• kmeantest.m - determine membership given clustering centers.

Radial Basis Network

• rbndemo.m: Demonstration program combining type 1 and type 2. Call rbn.m, gauss.m, cinit.m, kmeansf.m
• rbfexample1.m example 1 in rbf note. Call kernel1d.m

Time Series Analysis

• mademo.m -- Time series prediction demonstration program using the moving average (MA) model. Call mafun.m, and utility routines autocorr.m
• ardemo.m -- Time series prediction demonstration program using the auto-regressive (AR) model. Call utility routines autocorr.m and tsgenf.m

Fuzzy Logic

Fuzzy Relation Composition Demonstration

• fzrelationdemo.m -- Infer Mary's age from Dana's age, given that Dana is much_older than Mary
• fzcompose.m -- Max-min and Max- product fuzzy relationi composition rules

Fuzzy Logic Control Example: Dog Chases Cat

• dogcatfz.m -- Dog catch cat main program
• ruledc.m -- the rule base.
• fsgen.m -- Fussy set generation;
• fuzify.m -- Fuzzification routine;
• defuz.m -- Defuzzification routine;
• infer.m -- Fuzzy inference engine;

Genetic Algorithm demonstration: Travelling Salesperson Problem

• gademo.m -- demonstration of various solutions of TSA problem
• permugen.m -- generate all permutations of tours
• nbgen.m -- generate all permutations within a neighborhood of a specified distance
• rshift.m -- rotating shift of a vector

Utility Routines

• shadeplot.m -- illustrating how to plot a shaded area in a 2D region
• randomize.m -- randomize the row order of a matrix
• sline.m -- given a line function, compute the intersection of the line with the perimeter of a 2D box so that the line can be plotted. called by perceptron.m.
• datasepf.m -- generate two classes of data samples within unit square that are linearly separable.
• scale.m -- called by bp.m to linearly scale the input to a specified range.
• datagen.m -- 2D Gaussian Mixture data generation. May generate rotated, elliptic Gaussian clusteres. Labels can also be assigned. (Updated: 11/23/2005)datagen1.m -- variant of datagen.m.
• fungen.m -- generate 1 input, 1 output functions of (1) sinusoids; (2) piecewise linear functions; or (3) polynomials. fungenf.m -- callable function implementation of fungen.m
• polyfit1d.m -- fitting 1D data to a polynomial
• mydist.m -- calculate distance between two sets of points in L2, L1 and L_infinity norm
• autocorr.m -- calculate sample auto-correlation or autocovariance lags using rectangular window or triangular window.
• tsgenf.m -- generate time series and corresponding training and testing matrices. AR model, ligistic time series and rounding time series.

Data Sets

• xor -- XOR problem data file, M = 2, N = 1
• and -- AND problem data file, M = 2, N = 2
• parity4 -- 4-bit parity problem data file, M = 4, N = 1
• parity7 training data, and parity7 testing data -- 7-bit parity problem data file M = 7, N = 1
• IRIS training data IRIS testing data IRIS classification problem data file, M = 4, N = 3.