List of Matlab M-Files Used in Class

Last modification: December 03, 2001

These Matlab M-Files are written by Yu Hen Hu, and have been tested on Matlab V5.3. 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 .

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. Use mfiles mat2libsvm.m, readmodel.m, DOS programs svmtrain.exe as well as utility routines datasepf.m, datagen.m

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.

Utility Routines

• 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. 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
• dist.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.