This page lists ready to run shogun examples for the Static R interface.
To run the examples issue
R -f name_of_example.R
or start R and then type
source('name_of_example.R')
library("sg") size_cache <- 10 C <- 10 epsilon <- 1e-5 use_bias <- TRUE width <- 2.1 fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) label_train_multiclass <- as.real(as.matrix(read.table('../data/label_train_multiclass.dat'))) # GMNPSVM print('GMNPSVM') dump <- sg('set_features', 'TRAIN', fm_train_real) dump <- sg('set_kernel', 'GAUSSIAN', 'REAL', size_cache, width) dump <- sg('set_labels', 'TRAIN', label_train_multiclass) dump <- sg('new_classifier', 'GMNPSVM') dump <- sg('svm_epsilon', epsilon) dump <- sg('c', C) dump <- sg('svm_use_bias', use_bias) dump <- sg('train_classifier') dump <- sg('set_features', 'TEST', fm_test_real) result <- sg('classify')
library("sg") size_cache <- 10 C <- 10 epsilon <- 1e-5 use_bias <- TRUE width <- 2.1 fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) label_train_twoclass <- as.real(as.matrix(read.table('../data/label_train_twoclass.dat'))) # GPBTSVM print('GPBTSVM') dump <- sg('set_features', 'TRAIN', fm_train_real) dump <- sg('set_kernel', 'GAUSSIAN', 'REAL', size_cache, width) dump <- sg('set_labels', 'TRAIN', label_train_twoclass) dump <- sg('new_classifier', 'GPBTSVM') dump <- sg('svm_epsilon', epsilon) dump <- sg('c', C) dump <- sg('svm_use_bias', use_bias) dump <- sg('train_classifier') dump <- sg('set_features', 'TEST', fm_test_real) result <- sg('classify')
library("sg") fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) label_train_multiclass <- as.real(as.matrix(read.table('../data/label_train_multiclass.dat'))) # KNN print('KNN') k <- 3 dump <- sg('set_features', 'TRAIN', fm_train_real) dump <- sg('set_labels', 'TRAIN', label_train_multiclass) dump <- sg('set_distance', 'EUCLIDIAN', 'REAL') dump <- sg('new_classifier', 'KNN') dump <- sg('train_classifier', k) dump <- sg('set_features', 'TEST', fm_test_real) result <- sg('classify')
library("sg") fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) label_train_twoclass <- as.real(as.matrix(read.table('../data/label_train_twoclass.dat'))) # LDA print('LDA') dump <- sg('set_features', 'TRAIN', fm_train_real) dump <- sg('set_labels', 'TRAIN', label_train_twoclass) dump <- sg('new_classifier', 'LDA') dump <- sg('train_classifier') dump <- sg('set_features', 'TEST', fm_test_real) result <- sg('classify')
library("sg") size_cache <- 10 C <- 10 epsilon <- 1e-5 use_bias <- TRUE fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) label_train_twoclass <- as.real(as.matrix(read.table('../data/label_train_twoclass.dat'))) # LibSVM print('LibSVM') width <- 2.1 dump <- sg('set_features', 'TRAIN', fm_train_real) dump <- sg('set_kernel', 'GAUSSIAN', 'REAL', size_cache, width) dump <- sg('set_labels', 'TRAIN', label_train_twoclass) dump <- sg('new_classifier', 'LIBSVM') dump <- sg('svm_epsilon', epsilon) dump <- sg('c', C) dump <- sg('svm_use_bias', use_bias) dump <- sg('train_classifier') dump <- sg('set_features', 'TEST', fm_test_real) result <- sg('classify')
library("sg") size_cache <- 10 C <- 10 epsilon <- 1e-5 use_bias <- TRUE width <- 2.1 fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) label_train_multiclass <- as.real(as.matrix(read.table('../data/label_train_multiclass.dat'))) # LibSVM MultiClass print('LibSVMMultiClass') dump <- sg('set_features', 'TRAIN', fm_train_real) dump <- sg('set_kernel', 'GAUSSIAN', 'REAL', size_cache, width) dump <- sg('set_labels', 'TRAIN', label_train_multiclass) dump <- sg('new_classifier', 'LIBSVM_MULTICLASS') dump <- sg('svm_epsilon', epsilon) dump <- sg('c', C) dump <- sg('svm_use_bias', use_bias) dump <- sg('train_classifier') dump <- sg('set_features', 'TEST', fm_test_real) result <- sg('classify')
library("sg") size_cache <- 10 svm_nu <- 0.1 epsilon <- 1e-5 use_bias <- TRUE width <- 2.1 fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) # LibSVMOneClass print('LibSVMOneClass') dump <- sg('set_features', 'TRAIN', fm_train_real) dump <- sg('set_kernel', 'GAUSSIAN', 'REAL', size_cache, width) dump <- sg('new_classifier', 'LIBSVM_ONECLASS') dump <- sg('svm_epsilon', epsilon) dump <- sg('svm_nu', svm_nu) dump <- sg('svm_use_bias', use_bias) dump <- sg('train_classifier') dump <- sg('set_features', 'TEST', fm_test_real) result <- sg('classify')
library("sg") size_cache <- 10 C <- 10 epsilon <- 1e-5 use_bias <- TRUE width <- 2.1 fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) label_train_twoclass <- as.real(as.matrix(read.table('../data/label_train_twoclass.dat'))) # MPDSVM print('MPDSVM') dump <- sg('set_features', 'TRAIN', fm_train_real) dump <- sg('set_kernel', 'GAUSSIAN', 'REAL', size_cache, width) dump <- sg('set_labels', 'TRAIN', label_train_twoclass) dump <- sg('new_classifier', 'MPDSVM') dump <- sg('svm_epsilon', epsilon) dump <- sg('c', C) dump <- sg('svm_use_bias', use_bias) dump <- sg('train_classifier') dump <- sg('set_features', 'TEST', fm_test_real) result <- sg('classify')
library("sg") size_cache <- 10 C <- 10 epsilon <- 1e-5 use_bias <- TRUE fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) label_train_twoclass <- as.real(as.matrix(read.table('../data/label_train_twoclass.dat'))) # Perceptron print('Perceptron') dump <- sg('set_features', 'TRAIN', fm_train_real) dump <- sg('set_labels', 'TRAIN', label_train_twoclass) dump <- sg('new_classifier', 'PERCEPTRON') # often does not converge #dump <- sg('train_classifier') #dump <- sg('set_features', 'TEST', fm_test_real) #result <- sg('classify')
library("sg") size_cache <- 10 C <- 10 epsilon <- 1e-5 use_bias <- TRUE fm_train_dna <- as.matrix(read.table('../data/fm_train_dna.dat')) fm_test_dna <- as.matrix(read.table('../data/fm_test_dna.dat')) label_train_dna <- as.real(as.matrix(read.table('../data/label_train_dna.dat'))) degree <- 20 # SVM Light dosvmlight <- function() { print('SVMLight') dump <- sg('set_features', 'TRAIN', fm_train_dna, 'DNA') dump <- sg('set_kernel', 'WEIGHTEDDEGREE', 'CHAR', size_cache, degree) dump <- sg('set_labels', 'TRAIN', label_train_dna) dump <- sg('new_classifier', 'SVMLIGHT') dump <- sg('svm_epsilon', epsilon) dump <- sg('c', C) dump <- sg('svm_use_bias', use_bias) dump <- sg('train_classifier') dump <- sg('set_features', 'TEST', fm_test_dna, 'DNA') result <- sg('classify') } try(dosvmlight())
library("sg") fm_train <- as.matrix(read.table('../data/fm_train_real.dat')) # Hierarchical print('Hierarchical') merges=3 dump <- sg('set_features', 'TRAIN', fm_train) dump <- sg('set_distance', 'EUCLIDIAN', 'REAL') dump <- sg('new_clustering', 'HIERARCHICAL') dump <- sg('train_clustering', merges) result <- sg('get_clustering') merge_distances <- result[[1]] pairs <- result[[2]]
library("sg") fm_train <- as.matrix(read.table('../data/fm_train_real.dat')) # KMEANS print('KMeans') k <- 3 iter <- 1000 dump <- sg('set_distance', 'EUCLIDIAN', 'REAL') dump <- sg('set_features', 'TRAIN', fm_train) dump <- sg('new_clustering', 'KMEANS') dump <- sg('train_clustering', k, iter) result <- sg('get_clustering') radi <- result[[1]] centers <- result[[2]]
library("sg") fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) # BrayCurtis Distance print('BrayCurtisDistance') dump <- sg('set_distance', 'BRAYCURTIS', 'REAL') dump <- sg('set_features', 'TRAIN', fm_train_real) dm <- sg('get_distance_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_real) dm <- sg('get_distance_matrix', 'TEST')
library("sg") fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) # Canberra Metric print('CanberraMetric') dump <- sg('set_distance', 'CANBERRA', 'REAL') dump <- sg('set_features', 'TRAIN', fm_train_real) dm <- sg('get_distance_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_real) dm <- sg('get_distance_matrix', 'TEST')
library("sg") fm_train_dna <- as.matrix(read.table('../data/fm_train_dna.dat')) fm_test_dna <- as.matrix(read.table('../data/fm_test_dna.dat')) order <- 3 gap <- 0 reverse <- 'n' # Canberra Word Distance print('CanberraWordDistance') dump <- sg('set_distance', 'CANBERRA', 'WORD') dump <- sg('add_preproc', 'SORTWORDSTRING') dump <- sg('set_features', 'TRAIN', fm_train_dna, 'DNA') dump <- sg('convert', 'TRAIN', 'STRING', 'CHAR', 'STRING', 'WORD', order, order-1, gap, reverse) dump <- sg('attach_preproc', 'TRAIN') dm <- sg('get_distance_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_dna, 'DNA') dump <- sg('convert', 'TEST', 'STRING', 'CHAR', 'STRING', 'WORD', order, order-1, gap, reverse) dump <- sg('attach_preproc', 'TEST') dm <- sg('get_distance_matrix', 'TEST')
library("sg") fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) # Chebyshew Metric print('ChebyshewMetric') dump <- sg('set_distance', 'CHEBYSHEW', 'REAL') dump <- sg('set_features', 'TRAIN', fm_train_real) dm <- sg('get_distance_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_real) dm <- sg('get_distance_matrix', 'TEST')
library("sg") fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) # ChiSquare Distance print('ChiSquareDistance') dump <- sg('set_distance', 'CHISQUARE', 'REAL') dump <- sg('set_features', 'TRAIN', fm_train_real) dm <- sg('get_distance_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_real) dm <- sg('get_distance_matrix', 'TEST')
library("sg") fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) # Cosine Distance print('CosineDistance') dump <- sg('set_distance', 'COSINE', 'REAL') dump <- sg('set_features', 'TRAIN', fm_train_real) dm <- sg('get_distance_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_real) dm <- sg('get_distance_matrix', 'TEST')
library("sg") fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) # Euclidian Distance print('EuclidianDistance') dump <- sg('set_distance', 'EUCLIDIAN', 'REAL') dump <- sg('set_features', 'TRAIN', fm_train_real) dm <- sg('get_distance_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_real) dm <- sg('get_distance_matrix', 'TEST')
library("sg") fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) # Geodesic Metric print('GeodesicMetric') dump <- sg('set_distance', 'GEODESIC', 'REAL') dump <- sg('set_features', 'TRAIN', fm_train_real) dm <- sg('get_distance_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_real) dm <- sg('get_distance_matrix', 'TEST')
library("sg") fm_train_dna <- as.matrix(read.table('../data/fm_train_dna.dat')) fm_test_dna <- as.matrix(read.table('../data/fm_test_dna.dat')) order <- 3 gap <- 0 reverse <- 'n' # Hamming Word Distance print('HammingWordDistance') dump <- sg('set_distance', 'HAMMING', 'WORD') dump <- sg('add_preproc', 'SORTWORDSTRING') dump <- sg('set_features', 'TRAIN', fm_train_dna, 'DNA') dump <- sg('convert', 'TRAIN', 'STRING', 'CHAR', 'STRING', 'WORD', order, order-1, gap, reverse) dump <- sg('attach_preproc', 'TRAIN') dm <- sg('get_distance_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_dna, 'DNA') dump <- sg('convert', 'TEST', 'STRING', 'CHAR', 'STRING', 'WORD', order, order-1, gap, reverse) dump <- sg('attach_preproc', 'TEST') dm <- sg('get_distance_matrix', 'TEST')
library("sg") fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) # Jensen Metric print('JensenMetric') dump <- sg('set_distance', 'JENSEN', 'REAL') dump <- sg('set_features', 'TRAIN', fm_train_real) dm <- sg('get_distance_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_real) dm <- sg('get_distance_matrix', 'TEST')
library("sg") fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) # Manhattan Metric print('ManhattanMetric') dump <- sg('set_distance', 'MANHATTAN', 'REAL') dump <- sg('set_features', 'TRAIN', fm_train_real) dm <- sg('get_distance_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_real) dm <- sg('get_distance_matrix', 'TEST')
library("sg") fm_train_dna <- as.matrix(read.table('../data/fm_train_dna.dat')) fm_test_dna <- as.matrix(read.table('../data/fm_test_dna.dat')) order <- 3 gap <- 0 reverse <- 'n' # Manhattan Word Distance print('ManhattanWordDistance') dump <- sg('set_distance', 'MANHATTAN', 'WORD') dump <- sg('add_preproc', 'SORTWORDSTRING') dump <- sg('set_features', 'TRAIN', fm_train_dna, 'DNA') dump <- sg('convert', 'TRAIN', 'STRING', 'CHAR', 'STRING', 'WORD', order, order-1, gap, reverse) dump <- sg('attach_preproc', 'TRAIN') dm <- sg('get_distance_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_dna, 'DNA') dump <- sg('convert', 'TEST', 'STRING', 'CHAR', 'STRING', 'WORD', order, order-1, gap, reverse) dump <- sg('attach_preproc', 'TEST') dm <- sg('get_distance_matrix', 'TEST')
library("sg") fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) # Minkowski Metric print('MinkowskiMetric') k <- 3 dump <- sg('set_distance', 'MINKOWSKI', 'REAL', k) dump <- sg('set_features', 'TRAIN', fm_train_real) dm <- sg('get_distance_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_real) dm <- sg('get_distance_matrix', 'TEST')
library("sg") fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) # Tanimoto Distance print('TanimotoDistance') dump <- sg('set_distance', 'TANIMOTO', 'REAL') dump <- sg('set_features', 'TRAIN', fm_train_real) dm <- sg('get_distance_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_real) dm <- sg('get_distance_matrix', 'TEST')
library("sg") order <- 3 gap <- 0 reverse <- 'n' fm_train_dna <- as.matrix(read.table('../data/fm_train_dna.dat')) fm_train_cube <- as.matrix(read.table('../data/fm_train_cube.dat', colClasses=c('character'))) # # distributions # # Histogram print('Histogram') # sg('new_distribution', 'HISTOGRAM') dump <- sg('add_preproc', 'SORTWORDSTRING') dump <- sg('set_features', 'TRAIN', fm_train_dna, 'DNA') dump <- sg('convert', 'TRAIN', 'STRING', 'CHAR', 'STRING', 'WORD', order, order-1, gap, reverse) dump <- sg('attach_preproc', 'TRAIN') # sg('train_distribution') # histo=sg('get_histogram') # num_examples=11 # num_param=sg('get_histogram_num_model_parameters') # for i in xrange(num_examples): # for j in xrange(num_param): # sg('get_log_derivative %d %d' % (j, i)) # sg('get_log_likelihood') # sg('get_log_likelihood_sample')
library("sg") order <- 3 gap <- 0 reverse <- 'n' fm_train_dna <- as.matrix(read.table('../data/fm_train_dna.dat')) fm_train_cube <- as.matrix(read.table('../data/fm_train_cube.dat', colClasses=c('character'))) # HMM print('HMM') N <- 3 M <- 6 order <- 1 hmms <- c() liks <- c() dump <- sg('set_features', 'TRAIN', fm_train_cube, 'CUBE') dump <- sg('convert', 'TRAIN', 'STRING', 'CHAR', 'STRING', 'WORD', order) dump <- sg('new_hmm', N, M) dump <- sg('bw') hmm <- sg('get_hmm') dump <- sg('new_hmm', N, M) dump <- sg('set_hmm', hmm[[1]], hmm[[2]], hmm[[3]], hmm[[4]]) likelihood <- sg('hmm_likelihood')
library("sg") order <- 3 gap <- 0 reverse <- 'n' fm_train_dna <- as.matrix(read.table('../data/fm_train_dna.dat')) fm_train_cube <- as.matrix(read.table('../data/fm_train_cube.dat', colClasses=c('character'))) # Linear HMM print('LinearHMM') # sg('new_distribution', 'LinearHMM') dump <- sg('add_preproc', 'SORTWORDSTRING') dump <- sg('set_features', 'TRAIN', fm_train_dna, 'DNA') dump <- sg('convert', 'TRAIN', 'STRING', 'CHAR', 'STRING', 'WORD', order, order-1, gap, reverse) dump <- sg('attach_preproc', 'TRAIN') # sg('train_distribution') # histo=sg('get_histogram') # num_examples=11 # num_param=sg('get_histogram_num_model_parameters') # for i in xrange(num_examples): # for j in xrange(num_param): # sg('get_log_derivative %d %d' % (j, i)) # sg('get_log_likelihood') # sg('get_log_likelihood_sample')
library("sg") size_cache <- 10 fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) # CHI2 print('Chi2') width <- 1.4 dump <- sg('set_kernel', 'CHI2', 'REAL', size_cache, width) dump <- sg('set_features', 'TRAIN', fm_train_real) km <- sg('get_kernel_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_real) km <- sg('get_kernel_matrix', 'TEST')
library("sg") size_cache <- 10 fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) # Combined print('Combined') dump <- sg('clean_features', 'TRAIN') dump <- sg('clean_features', 'TEST') dump <- sg('set_kernel', 'COMBINED', size_cache) dump <- sg('add_kernel', 1, 'LINEAR', 'REAL', size_cache) dump <- sg('add_features', 'TRAIN', fm_train_real) dump <- sg('add_features', 'TEST', fm_test_real) dump <- sg('add_kernel', 1, 'GAUSSIAN', 'REAL', size_cache, 1) dump <- sg('add_features', 'TRAIN', fm_train_real) dump <- sg('add_features', 'TEST', fm_test_real) dump <- sg('add_kernel', 1, 'POLY', 'REAL', size_cache, 3, FALSE) dump <- sg('add_features', 'TRAIN', fm_train_real) dump <- sg('add_features', 'TEST', fm_test_real) km <- sg('get_kernel_matrix', 'TRAIN') km <- sg('get_kernel_matrix', 'TEST')
library("sg") size_cache <- 10 fm_train_dna <- as.matrix(read.table('../data/fm_train_dna.dat')) fm_test_dna <- as.matrix(read.table('../data/fm_test_dna.dat')) order <- 3 gap <- 0 reverse <- 'n' use_sign <- FALSE normalization <- 'FULL' # Comm Ulong String print('CommUlongString') dump <- sg('add_preproc', 'SORTULONGSTRING') dump <- sg('set_kernel', 'COMMSTRING', 'ULONG', size_cache, use_sign, normalization) dump <- sg('set_features', 'TRAIN', fm_train_dna, 'DNA') dump <- sg('convert', 'TRAIN', 'STRING', 'CHAR', 'STRING', 'ULONG', order, order-1, gap, reverse) dump <- sg('attach_preproc', 'TRAIN') km <- sg('get_kernel_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_dna, 'DNA') dump <- sg('convert', 'TEST', 'STRING', 'CHAR', 'STRING', 'ULONG', order, order-1, gap, reverse) dump <- sg('attach_preproc', 'TEST') km <- sg('get_kernel_matrix', 'TEST')
library("sg") size_cache <- 10 fm_train_dna <- as.matrix(read.table('../data/fm_train_dna.dat')) fm_test_dna <- as.matrix(read.table('../data/fm_test_dna.dat')) order <- 3 gap <- 0 reverse <- 'n' use_sign <- FALSE normalization <- 'FULL' # Comm Word String print('CommWordString') dump <- sg('add_preproc', 'SORTWORDSTRING') dump <- sg('set_kernel', 'COMMSTRING', 'WORD', size_cache, use_sign, normalization) dump <- sg('set_features', 'TRAIN', fm_train_dna, 'DNA') dump <- sg('convert', 'TRAIN', 'STRING', 'CHAR', 'STRING', 'WORD', order, order-1, gap, reverse) dump <- sg('attach_preproc', 'TRAIN') km <- sg('get_kernel_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_dna, 'DNA') dump <- sg('convert', 'TEST', 'STRING', 'CHAR', 'STRING', 'WORD', order, order-1, gap, reverse) dump <- sg('attach_preproc', 'TEST') km <- sg('get_kernel_matrix', 'TEST')
library("sg") size_cache <- 10 fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) # Const print('Const') c <- 23. dump <- sg('set_kernel', 'CONST', 'REAL', size_cache, c) dump <- sg('set_features', 'TRAIN', fm_train_real) km <- sg('get_kernel_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_real) km <- sg('get_kernel_matrix', 'TEST')
library("sg") size_cache <- 10 fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) # Diag print('Diag') diag=23. dump <- sg('set_kernel', 'DIAG', 'REAL', size_cache, diag) dump <- sg('set_features', 'TRAIN', fm_train_real) km <- sg('get_kernel_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_real) km <- sg('get_kernel_matrix', 'TEST')
library("sg") size_cache <- 10 fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) # Distance print('Distance') width=1.7 dump <- sg('set_distance', 'EUCLIDIAN', 'REAL') dump <- sg('set_kernel', 'DISTANCE', size_cache, width) dump <- sg('set_features', 'TRAIN', fm_train_real) km=sg('get_kernel_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_real) km=sg('get_kernel_matrix', 'TEST')
library("sg") size_cache <- 10 fm_train_dna <- as.matrix(read.table('../data/fm_train_dna.dat')) fm_test_dna <- as.matrix(read.table('../data/fm_test_dna.dat')) # Fixed Degree String print('FixedDegreeString') degree <- 3 dump <- sg('set_kernel', 'FIXEDDEGREE', 'CHAR', size_cache, degree) dump <- sg('set_features', 'TRAIN', fm_train_dna, 'DNA') km <- sg('get_kernel_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_dna, 'DNA') km <- sg('get_kernel_matrix', 'TEST')
library("sg") size_cache <- 10 fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) # Gaussian print('Gaussian') width <- 1.9 dump <- sg('set_kernel', 'GAUSSIAN', 'REAL', size_cache, width) dump <- sg('set_features', 'TRAIN', fm_train_real) km <- sg('get_kernel_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_real) km <- sg('get_kernel_matrix', 'TEST')
library("sg") size_cache <- 10 fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) # GaussianShift print('GaussianShift') width <- 1.8 max_shift <- 2 shift_step <- 1 dump <- sg('set_kernel', 'GAUSSIANSHIFT', 'REAL', size_cache, width, max_shift, shift_step) dump <- sg('set_features', 'TRAIN', fm_train_real) km <- sg('get_kernel_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_real) km <- sg('get_kernel_matrix', 'TEST')
library("sg") size_cache <- 10 order <- 3 gap <- 0 reverse <- 'n' fm_train_dna <- as.matrix(read.table('../data/fm_train_dna.dat')) fm_test_dna <- as.matrix(read.table('../data/fm_test_dna.dat')) label_train_dna <- as.real(as.matrix(read.table('../data/label_train_dna.dat'))) # PluginEstimate print('PluginEstimate w/ HistogramWord') dump <- sg('set_features', 'TRAIN', fm_train_dna, 'DNA') dump <- sg('convert', 'TRAIN', 'STRING', 'CHAR', 'STRING', 'WORD', order, order-1, gap, reverse) dump <- sg('set_features', 'TEST', fm_test_dna, 'DNA') dump <- sg('convert', 'TEST', 'STRING', 'CHAR', 'STRING', 'WORD', order, order-1, gap, reverse) pseudo_pos <- 1e-1 pseudo_neg <- 1e-1 dump <- sg('new_plugin_estimator', pseudo_pos, pseudo_neg) dump <- sg('set_labels', 'TRAIN', label_train_dna) dump <- sg('train_estimator') dump <- sg('set_kernel', 'HISTOGRAM', 'WORD', size_cache) km <- sg('get_kernel_matrix', 'TRAIN') # not supported yet # lab=sg('plugin_estimate_classify') km <- sg('get_kernel_matrix', 'TEST')
library("sg") size_cache <- 10 fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) # Linear print('Linear') scale <- 1.2 dump <- sg('set_kernel', 'LINEAR', 'REAL', size_cache, scale) dump <- sg('set_features', 'TRAIN', fm_train_real) km <- sg('get_kernel_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_real) km <- sg('get_kernel_matrix', 'TEST')
library("sg") size_cache <- 10 fm_train_dna <- as.matrix(read.table('../data/fm_train_dna.dat')) fm_test_dna <- as.matrix(read.table('../data/fm_test_dna.dat')) # Linear String print('LinearString') dump <- sg('set_kernel', 'LINEAR', 'CHAR', size_cache) dump <- sg('set_features', 'TRAIN', fm_train_dna, 'DNA') km <- sg('get_kernel_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_dna, 'DNA') km <- sg('get_kernel_matrix', 'TEST')
library("sg") size_cache <- 10 fm_train_dna <- as.matrix(read.table('../data/fm_train_dna.dat')) fm_test_dna <- as.matrix(read.table('../data/fm_test_dna.dat')) # Local Alignment String print('LocalAlignmentString') dump <- sg('set_kernel', 'LOCALALIGNMENT', 'CHAR', size_cache) dump <- sg('set_features', 'TRAIN', fm_train_dna, 'DNA') km <- sg('get_kernel_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_dna, 'DNA') km <- sg('get_kernel_matrix', 'TEST')
library("sg") size_cache <- 10 fm_train_dna <- as.matrix(read.table('../data/fm_train_dna.dat')) fm_test_dna <- as.matrix(read.table('../data/fm_test_dna.dat')) # Locality Improved String print('LocalityImprovedString') length <- 5 inner_degree <- 5 outer_degree <- inner_degree+2 dump <- sg('set_kernel', 'LIK', 'CHAR', size_cache, length, inner_degree, outer_degree) dump <- sg('set_features', 'TRAIN', fm_train_dna, 'DNA') km <- sg('get_kernel_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_dna, 'DNA') km <- sg('get_kernel_matrix', 'TEST')
library("sg") size_cache <- 10 fm_train_dna <- as.matrix(read.table('../data/fm_train_dna.dat')) fm_test_dna <- as.matrix(read.table('../data/fm_test_dna.dat')) # Oligo String print('OligoString') k <- 3 width <- 1.2 dump <- sg('set_kernel', 'OLIGO', 'CHAR', size_cache, k, width) dump <- sg('set_features', 'TRAIN', fm_train_dna, 'DNA') km <- sg('get_kernel_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_dna, 'DNA') km <- sg('get_kernel_matrix', 'TEST')
library("sg") size_cache <- 10 fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) # Poly print('Poly') degree <- 4 inhomogene <- FALSE use_normalization <- TRUE dump <- sg('set_kernel', 'POLY', 'REAL', size_cache, degree, inhomogene, use_normalization) dump <- sg('set_features', 'TRAIN', fm_train_real) km <- sg('get_kernel_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_real) km <- sg('get_kernel_matrix', 'TEST')
library("sg") size_cache <- 10 fm_train_dna <- as.matrix(read.table('../data/fm_train_dna.dat')) fm_test_dna <- as.matrix(read.table('../data/fm_test_dna.dat')) # Poly Match String print('PolyMatchString') degree <- 3 inhomogene <- FALSE dump <- sg('set_kernel', 'POLYMATCH', 'CHAR', size_cache, degree, inhomogene) dump <- sg('set_features', 'TRAIN', fm_train_dna, 'DNA') km <- sg('get_kernel_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_dna, 'DNA') km <- sg('get_kernel_matrix', 'TEST')
library("sg") size_cache <- 10 fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) # Sigmoid print('Sigmoid') gamma <- 1.2 coef0 <- 1.3 dump <- sg('set_kernel', 'SIGMOID', 'REAL', size_cache, gamma, coef0) dump <- sg('set_features', 'TRAIN', fm_train_real) km <- sg('get_kernel_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_real) km <- sg('get_kernel_matrix', 'TEST')
library("sg") size_cache <- 10 fm_train_dna <- as.matrix(read.table('../data/fm_train_dna.dat')) fm_test_dna <- as.matrix(read.table('../data/fm_test_dna.dat')) # Simple Locality Improved String print('SimpleLocalityImprovedString') length <- 5 inner_degree <- 5 outer_degree <- inner_degree+2 dump <- sg('set_kernel', 'SLIK', 'CHAR', size_cache, length, inner_degree, outer_degree) dump <- sg('set_features', 'TRAIN', fm_train_dna, 'DNA') km <- sg('get_kernel_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_dna, 'DNA') km <- sg('get_kernel_matrix', 'TEST')
library(sg) traindat = c("AGTAA", "CGCCC", "GGCGG", "TGTCT") trainlab <- c(1,-1,-1,1) testdat = c("AGCAA", "CCCCC", "GGGGG", "TGCTT") order = 2 C = 1.0 sg('loglevel', 'ALL') sg('use_linadd', TRUE) sg('mkl_parameters', 1e-5, 0) sg('svm_epsilon', 1e-4) sg('clean_features', 'TRAIN') sg('clean_kernel') sg('set_features', 'TRAIN', traindat, 'DNA') sg('convert', 'TRAIN', 'STRING', 'CHAR', 'STRING', 'WORD', order, order-1) sg('add_preproc', 'SORTWORDSTRING') sg('attach_preproc', 'TRAIN') sg('set_labels', 'TRAIN', trainlab) sg('new_classifier', 'SVMLIGHT') sg('set_kernel', 'COMMSTRING', 'WORD', 10, TRUE, 'FULL') sg('c', C) km=sg('get_kernel_matrix', 'TRAIN') sg('train_classifier') svmAsList=sg('get_svm') sg('set_features', 'TEST', testdat, 'DNA') sg('convert', 'TEST', 'STRING', 'CHAR', 'STRING', 'WORD', order, order-1) sg('attach_preproc', 'TEST') sg('init_kernel_optimization') valout=sg('classify')
library("sg") size_cache <- 10 fm_train_dna <- as.matrix(read.table('../data/fm_train_dna.dat')) fm_test_dna <- as.matrix(read.table('../data/fm_test_dna.dat')) order <- 3 gap <- 0 reverse <- 'n' use_sign <- FALSE normalization <- 'FULL' # Weighted Comm Word String print('WeightedCommWordString') dump <- sg('add_preproc', 'SORTWORDSTRING') dump <- sg('set_kernel', 'WEIGHTEDCOMMSTRING', 'WORD', size_cache, use_sign, normalization) dump <- sg('set_features', 'TRAIN', fm_train_dna, 'DNA') dump <- sg('convert', 'TRAIN', 'STRING', 'CHAR', 'STRING', 'WORD', order, order-1, gap, reverse) dump <- sg('attach_preproc', 'TRAIN') km <- sg('get_kernel_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_dna, 'DNA') dump <- sg('convert', 'TEST', 'STRING', 'CHAR', 'STRING', 'WORD', order, order-1, gap, reverse) dump <- sg('attach_preproc', 'TEST') km <- sg('get_kernel_matrix', 'TEST')
library("sg") size_cache <- 10 fm_train_dna <- as.matrix(read.table('../data/fm_train_dna.dat')) fm_test_dna <- as.matrix(read.table('../data/fm_test_dna.dat')) # Weighted Degree Position String print('WeightedDegreePositionString') degree <- 20 dump <- sg('set_kernel', 'WEIGHTEDDEGREEPOS', 'CHAR', size_cache, degree) dump <- sg('set_features', 'TRAIN', fm_train_dna, 'DNA') km <- sg('get_kernel_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_dna, 'DNA') km <- sg('get_kernel_matrix', 'TEST')
library("sg") size_cache <- 10 fm_train_dna <- as.matrix(read.table('../data/fm_train_dna.dat')) fm_test_dna <- as.matrix(read.table('../data/fm_test_dna.dat')) # Weighted Degree String print('WeightedDegreeString') degree <- 20 dump <- sg('set_kernel', 'WEIGHTEDDEGREE', 'CHAR', size_cache, degree) dump <- sg('set_features', 'TRAIN', fm_train_dna, 'DNA') km <- sg('get_kernel_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_dna, 'DNA') km <- sg('get_kernel_matrix', 'TEST')
# This script should enable you to rerun the experiment in the # paper that we labeled with "christmas star". # # The task is to classify two star-shaped classes that share the # midpoint. The difficulty of the learning problem depends on the # distance between the classes, which is varied # # Our model selection leads to a choice of C <- 0.5. The model # selection is not repeated inside this script. library(sg) # Preliminary settings: C <- 0.5 # SVM Parameter cache_size <- 50 # cache per kernel in MB svm_eps<-1e-3 # svm epsilon mkl_eps<-1e-3 # mkl epsilon no_obs <- 20 # number of observations / data points (sum for train and test and both classes) k_star <- 20 # number of "leaves" of the stars alpha <- 0.3 # noise level of the data radius_star <- matrix(0, length(seq(4.1, 10, 0.2)), 2) radius_star[,1] <- seq(4.1, 10, 0.2) # increasing radius of the 1.class radius_star[,2] <- matrix(4, length(radius_star[,1]),1) # fixed radius 2.class # distanz between the classes: diff(radius_star(:,1)-radius_star(:,2)) rbf_width <- c(0.01, 0.1, 1, 10, 1000) # different width for the five used rbf kernels #### #### Great loop: train MKL for every data set (the different distances between the stars) #### sg('loglevel', 'ERROR') sg('echo', 'OFF') w = matrix(0, length(1:dim(radius_star)[1]), length(rbf_width)) result.trainout=matrix(0, length(1:dim(radius_star)[1]), 2*no_obs) result.testout=matrix(0, length(1:dim(radius_star)[1]), 2*no_obs) result.trainerr=matrix(0,length(rbf_width), 1) result.testerr=matrix(0,length(rbf_width), 1) for (kk in 1:dim(radius_star)[1]) { # data generation print(sprintf('MKL for radius %+02.2f ', radius_star[kk,1])) dummy <- matrix(0, 2, 4*no_obs) dummy[1,] <- runif(4*no_obs) noise <- alpha*rnorm(4*no_obs) dummy[2,] <- sin(k_star*pi*dummy[1,]) + noise # sine dummy[2,1:(2*no_obs)] <- dummy[2,1:(2*no_obs)]+ radius_star[kk,1] # distanz shift: first class dummy[2,(2*no_obs+1):dim(dummy)[2]] <- dummy[2,(2*no_obs+1):dim(dummy)[2]]+ radius_star[kk,2] # distanz shift: second class dummy[1,] <- 2*pi*dummy[1,] x <- matrix(0, dim(dummy)[1], dim(dummy)[2]) x[1,] <- dummy[2,]*sin(dummy[1,]) x[2,] <- dummy[2,]*cos(dummy[1,]) train_y <- c(-matrix(1,1, no_obs), matrix(1,1,no_obs)) test_y <- c(-matrix(1,1, no_obs), matrix(1,1,no_obs)) train_x <- matrix(0, 0, seq(1,dim(x)[2]/2)) train_x <- x[,seq(1,dim(x)[2],2)] test_x <- x[,seq(2,dim(x)[2],2)] rm('dummy', 'x') # train MKL sg('clean_kernel') sg('clean_features', 'TRAIN') sg('add_features','TRAIN', train_x) # set a trainingset for every SVM sg('add_features','TRAIN', train_x) sg('add_features','TRAIN', train_x) sg('add_features','TRAIN', train_x) sg('add_features','TRAIN', train_x) sg('set_labels','TRAIN', train_y) # set the labels sg('new_classifier', 'MKL_CLASSIFICATION') sg('mkl_parameters', mkl_eps, 0) sg('svm_epsilon', svm_eps) sg('set_kernel', 'COMBINED', 0) sg('add_kernel', 1, 'GAUSSIAN', 'REAL', cache_size, rbf_width[1]) sg('add_kernel', 1, 'GAUSSIAN', 'REAL', cache_size, rbf_width[2]) sg('add_kernel', 1, 'GAUSSIAN', 'REAL', cache_size, rbf_width[3]) sg('add_kernel', 1, 'GAUSSIAN', 'REAL', cache_size, rbf_width[4]) sg('add_kernel', 1, 'GAUSSIAN', 'REAL', cache_size, rbf_width[5]) sg('c', C) sg('train_classifier') alphas <- sg('get_svm')[2] w[kk,] <- sg('get_subkernel_weights') # calculate train error sg('clean_features', 'TEST') sg('add_features','TEST',train_x) sg('add_features','TEST',train_x) sg('add_features','TEST',train_x) sg('add_features','TEST',train_x) sg('add_features','TEST',train_x) sg('set_labels','TEST', train_y) sg('set_threshold', 0) result.trainout[kk,]<-sg('classify') result.trainerr[kk] <- mean(train_y!=sign(result.trainout[kk,])) # calculate test error sg('clean_features', 'TEST') sg('add_features','TEST',test_x) sg('add_features','TEST',test_x) sg('add_features','TEST',test_x) sg('add_features','TEST',test_x) sg('add_features','TEST',test_x) sg('set_labels','TEST',test_y) sg('set_threshold', 0) result.testout[kk,]<-sg('classify') result.testerr[kk] <- mean(test_y!=sign(result.testout[kk,])) } cat('done. now w contains the kernel weightings and result test/train outputs and errors')
library("sg") size_cache <- 10 C <- 1.2 epsilon <- 1e-5 mkl_eps <- 0.01 mkl_norm <- 1 width <- 1.2 fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) label_train_multiclass <- as.real(as.matrix(read.table('../data/label_train_multiclass.dat'))) # MKL_MULTICLASS print('MKL_MULTICLASS') dump <- sg('clean_features', 'TRAIN') dump <- sg('clean_features', 'TEST') dump <- sg('set_kernel', 'COMBINED', size_cache) dump <- sg('add_kernel', 1, 'LINEAR', 'REAL', size_cache) dump <- sg('add_features', 'TRAIN', fm_train_real) dump <- sg('add_features', 'TEST', fm_test_real) dump <- sg('add_kernel', 1, 'GAUSSIAN', 'REAL', size_cache, width) dump <- sg('add_features', 'TRAIN', fm_train_real) dump <- sg('add_features', 'TEST', fm_test_real) dump <- sg('add_kernel', 1, 'POLY', 'REAL', size_cache, 2) dump <- sg('add_features', 'TRAIN', fm_train_real) dump <- sg('add_features', 'TEST', fm_test_real) dump <- sg('set_labels', 'TRAIN', label_train_multiclass) dump <- sg('new_classifier', 'MKL_MULTICLASS') dump <- sg('svm_epsilon', epsilon) dump <- sg('c', C) dump <- sg('mkl_parameters', mkl_eps, 0, mkl_norm); dump <- sg('train_classifier') result <- sg('classify')
# This script should enable you to rerun the experiment in the # paper that we labeled "mixture linear and sine ". # # The task is to learn a regression function where the true function # is given by a mixture of 2 sine waves in addition to a linear trend. # We vary the frequency of the second higher frequency sine wave. # Setup: MKL on 10 RBF kernels of different widths on 1000 examples #load shogun library(sg) # kernel width for 10 basic SVMs rbf_width <- array(0.0, dim<-c(1,10)) rbf_width[1] <- 0.001 rbf_width[2] <- 0.005 rbf_width[3] <- 0.01 rbf_width[4] <- 0.05 rbf_width[5] <- 0.1 rbf_width[6] <- 1 rbf_width[7] <- 10 rbf_width[8] <- 50 rbf_width[9] <- 100 rbf_width[10] <- 1000 # SVM parameter C <- 1 cache_size <- 50 mkl_eps <- 1e-4 svm_eps <- 1e-4 svm_tube <- 0.01 debug <- 0 # data f <- c(0:20) # parameter that varies the frequency of the second sine wave #sg('loglevel', 'ALL') #sg('echo', 'ON') weights <- array(dim<-c(21,10)) no_obs <- 10 # number of observations stepsize <- (4*pi)/(no_obs-1) train_x <- c(0:(no_obs-1)) for (i in c(1:no_obs)) { train_x[i] <- train_x[i] * stepsize } trend <- 2 * train_x* ((pi)/(max(train_x)-min(train_x))) wave1 <- sin(train_x) wave2 <- sin(f[1]*train_x) train_y <- trend + wave1 + wave2 train_x<-matrix(train_x,1, length(train_x)) weights=matrix(0, length(f), length(rbf_width)) for (kk in c(1:length(f))) { #Big loop #data generation wave1 <- sin(train_x) wave2 <- sin(f[kk]*train_x) train_y <- trend + wave1 + wave2 #MK Learning sg('new_classifier', 'MKL_REGRESSION') sg('mkl_parameters', mkl_eps, 0) sg('c', C) sg('svm_epsilon', svm_eps) sg('svr_tube_epsilon', svm_tube) sg('clean_features', 'TRAIN') sg('clean_kernel') sg('set_labels', 'TRAIN', train_y) #set labels sg('add_features', 'TRAIN', train_x) #add features for every basic SVM sg('add_features', 'TRAIN', train_x) sg('add_features', 'TRAIN', train_x) sg('add_features', 'TRAIN', train_x) sg('add_features', 'TRAIN', train_x) sg('add_features', 'TRAIN', train_x) sg('add_features', 'TRAIN', train_x) sg('add_features', 'TRAIN', train_x) sg('add_features', 'TRAIN', train_x) sg('add_features', 'TRAIN', train_x) sg('set_kernel', 'COMBINED', 0) sg('add_kernel', 1, 'GAUSSIAN', 'REAL', cache_size, rbf_width[1]) sg('add_kernel', 1, 'GAUSSIAN', 'REAL', cache_size, rbf_width[2]) sg('add_kernel', 1, 'GAUSSIAN', 'REAL', cache_size, rbf_width[3]) sg('add_kernel', 1, 'GAUSSIAN', 'REAL', cache_size, rbf_width[4]) sg('add_kernel', 1, 'GAUSSIAN', 'REAL', cache_size, rbf_width[5]) sg('add_kernel', 1, 'GAUSSIAN', 'REAL', cache_size, rbf_width[6]) sg('add_kernel', 1, 'GAUSSIAN', 'REAL', cache_size, rbf_width[7]) sg('add_kernel', 1, 'GAUSSIAN', 'REAL', cache_size, rbf_width[8]) sg('add_kernel', 1, 'GAUSSIAN', 'REAL', cache_size, rbf_width[9]) sg('add_kernel', 1, 'GAUSSIAN', 'REAL', cache_size, rbf_width[10]) sg('train_classifier') weights[kk,] <- get_subkernel_weights() cat("frequency:", f[kk], " rbf-kernel-weights: ", weights[kk,], "\n") }
# This script should enable you to rerun the experiment in the # paper that we labeled "sine". # # In this regression task a sine wave is to be learned. # We vary the frequency of the wave. # Preliminary settings: library(sg) # Parameter for the SVMs. C <- 10 # obtained via model selection (not included in the script) cache_size <- 10 mkl_eps <- 1e-3 # threshold for precision svm_eps <- 1e-3 svr_tube_eps <- 1e-2 debug <- 0 # Kernel width for the 5 "basic" SVMs rbf_width <- c(0.005, 0.05, 0.5, 1, 10) # data f <- c(0.1:0.2:5) # values for the different frequencies no_obs <- 10 # number of observations if (debug) { sg('loglevel', 'ALL'); sg('echo', 'ON'); } else { sg('loglevel', 'ERROR'); sg('echo', 'OFF') } weights=matrix(0, length(f), length(rbf_width)) for (kk in 1:length(f)) { # big loop for the different learning problems # data generation train_x <- seq(1,10*2*pi, (((10*2*pi)-1)/(no_obs-1))) train_y <- sin(f[kk]*train_x) train_x <- matrix(train_x, 1, length(train_x)) # initialize MKL-SVR sg('new_classifier', 'MKL_REGRESSION') sg('mkl_parameters', mkl_eps, 0) sg('c', C) sg('svm_epsilon', svm_eps) sg('svr_tube_epsilon', svr_tube_eps) sg('clean_features', 'TRAIN') sg('clean_kernel') sg('set_labels', 'TRAIN', train_y) # set labels sg('add_features', 'TRAIN', train_x) # add features for every SVR sg('add_features', 'TRAIN', train_x) sg('add_features', 'TRAIN', train_x) sg('add_features', 'TRAIN', train_x) sg('add_features', 'TRAIN', train_x) sg('set_kernel', 'COMBINED', 0) sg('add_kernel', 1, 'GAUSSIAN', 'REAL', cache_size, rbf_width[1]) sg('add_kernel', 1, 'GAUSSIAN', 'REAL', cache_size, rbf_width[2]) sg('add_kernel', 1, 'GAUSSIAN', 'REAL', cache_size, rbf_width[3]) sg('add_kernel', 1, 'GAUSSIAN', 'REAL', cache_size, rbf_width[4]) sg('add_kernel', 1, 'GAUSSIAN', 'REAL', cache_size, rbf_width[5]) sg('svm_train') weights[kk,] <- sg('get_subkernel_weights') dummy <- print(sprintf('frequency: %02.2f rbf-kernel-weights: %02.2f %02.2f %02.2f %02.2f %02.2f', f[kk], weights[kk,1], weights[kk,2], weights[kk,3], weights[kk,4], weights[kk,5])) }
library(sg) acgt <- c("A","C","G","T") LT=sign(rnorm(1000)) XT= array("",dim=c(100,1000)) for (i in 1:length(XT)) { XT[i] = acgt[ceiling(4 * (rnorm(1) %% 1))] } for (k in c(30,60,61)) { for (i in 1:length(XT[k,])) { if (LT[i] == 1) { XT[k,i] = "A" } } } idx=sample(c(1:1000)) XTE=XT[,idx[1:200]] LTE=LT[idx[1:200]] XT=XT[,idx[201:1000]] LT=LT[idx[201:1000]] center_idx = 50 degree=3 mismatch = 0 C=1 #sg('loglevel', 'ALL') sg('use_linadd', TRUE) sg('mkl_parameters', 1e-5, 1) sg('svm_epsilon', 1e-6) sg('clean_features', 'TRAIN') sg('clean_kernel') sg('new_classifier', 'MKL_CLASSIFICATION') sg('set_labels', 'TRAIN', LT) sg('set_features', 'TRAIN', XT, 'DNA') sg('set_kernel', 'WEIGHTEDDEGREE', 'CHAR', 10, degree, mismatch, FALSE, 1) sg('c', C) sg('svm_train') svmAsList=sg('get_svm') beta=sg('get_subkernel_weights') sg('init_kernel_optimization') sg('clean_features', 'TEST') sg('set_features', 'TEST', XTE, 'DNA') output_xte = sg('classify') w=sg('get_subkernel_weights') err=mean(sign(output_xte)!=LTE)
library("sg") size_cache <- 10 fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) width <- 1.4 # LogPlusOne print('LogPlusOne') dump <- sg('add_preproc', 'LOGPLUSONE') dump <- sg('set_kernel', 'CHI2', 'REAL', size_cache, width) dump <- sg('set_features', 'TRAIN', fm_train_real) dump <- sg('attach_preproc', 'TRAIN') km <- sg('get_kernel_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_real) dump <- sg('attach_preproc', 'TEST') km <- sg('get_kernel_matrix', 'TEST')
library("sg") size_cache <- 10 width <- 2.1 fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) fm_train_dna <- as.matrix(read.table('../data/fm_train_dna.dat')) fm_test_dna <- as.matrix(read.table('../data/fm_test_dna.dat')) # NormOne print('NormOne') dump <- sg('add_preproc', 'NORMONE') dump <- sg('set_kernel', 'CHI2', 'REAL', size_cache, width) dump <- sg('set_features', 'TRAIN', fm_train_real) dump <- sg('attach_preproc', 'TRAIN') km <- sg('get_kernel_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_real) dump <- sg('attach_preproc', 'TEST') km <- sg('get_kernel_matrix', 'TEST')
library("sg") size_cache <- 10 width <- 2.1 fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat')) # PruneVarSubMean print('PruneVarSubMean') divide_by_std <- TRUE dump <- sg('add_preproc', 'PRUNEVARSUBMEAN', divide_by_std) dump <- sg('set_kernel', 'CHI2', 'REAL', size_cache, width) dump <- sg('set_features', 'TRAIN', fm_train_real) dump <- sg('attach_preproc', 'TRAIN') km <- sg('get_kernel_matrix', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_real) dump <- sg('attach_preproc', 'TEST') km <- sg('get_kernel_matrix', 'TEST')
library("sg") size_cache <- 10 fm_train_dna <- as.matrix(read.table('../data/fm_train_dna.dat')) fm_test_dna <- as.matrix(read.table('../data/fm_test_dna.dat')) order <- 3 gap <- 0 reverse <- 'n' use_sign <- FALSE normalization <- 'FULL' # Comm Ulong String print('CommUlongString') dump <- sg('add_preproc', 'SORTULONGSTRING') dump <- sg('set_features', 'TRAIN', fm_train_dna, 'DNA') dump <- sg('convert', 'TRAIN', 'STRING', 'CHAR', 'STRING', 'ULONG', order, order-1, gap, reverse) dump <- sg('attach_preproc', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_dna, 'DNA') dump <- sg('convert', 'TEST', 'STRING', 'CHAR', 'STRING', 'ULONG', order, order-1, gap, reverse) dump <- sg('attach_preproc', 'TEST') dump <- sg('set_kernel', 'COMMSTRING', 'ULONG', size_cache, use_sign, normalization) km <- sg('get_kernel_matrix', 'TRAIN') km <- sg('get_kernel_matrix', 'TEST')
library("sg") size_cache <- 10 fm_train_dna <- as.matrix(read.table('../data/fm_train_dna.dat')) fm_test_dna <- as.matrix(read.table('../data/fm_test_dna.dat')) order <- 3 gap <- 0 reverse <- 'n' use_sign <- FALSE normalization <- 'FULL' # Comm Word String print('CommWordString') dump <- sg('add_preproc', 'SORTWORDSTRING') dump <- sg('set_features', 'TRAIN', fm_train_dna, 'DNA') dump <- sg('convert', 'TRAIN', 'STRING', 'CHAR', 'STRING', 'WORD', order, order-1, gap, reverse) dump <- sg('attach_preproc', 'TRAIN') dump <- sg('set_features', 'TEST', fm_test_dna, 'DNA') dump <- sg('convert', 'TEST', 'STRING', 'CHAR', 'STRING', 'WORD', order, order-1, gap, reverse) dump <- sg('attach_preproc', 'TEST') dump <- sg('set_kernel', 'COMMSTRING', 'WORD', size_cache, use_sign, normalization) km <- sg('get_kernel_matrix', 'TRAIN') km <- sg('get_kernel_matrix', 'TEST')
library("sg") size_cache <- 10 C <- 10 tube_epsilon <- 1e-2 width <- 2.1 fm_train <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test <- as.matrix(read.table('../data/fm_test_real.dat')) label_train <- as.real(as.matrix(read.table('../data/label_train_twoclass.dat'))) # KRR print('KRR') tau <- 1e-6 dump <- sg('set_features', 'TRAIN', fm_train) dump <- sg('set_kernel', 'GAUSSIAN', 'REAL', size_cache, width) dump <- sg('set_labels', 'TRAIN', label_train) dump <- sg('new_regression', 'KRR') dump <- sg('krr_tau', tau) dump <- sg('c', C) dump <- sg('train_regression') dump <- sg('set_features', 'TEST', fm_test) result <- sg('classify')
library("sg") size_cache <- 10 C <- 10 tube_epsilon <- 1e-2 width <- 2.1 fm_train <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test <- as.matrix(read.table('../data/fm_test_real.dat')) label_train <- as.real(as.matrix(read.table('../data/label_train_twoclass.dat'))) # LibSVR print('LibSVR') dump <- sg('set_features', 'TRAIN', fm_train) dump <- sg('set_kernel', 'GAUSSIAN', 'REAL', size_cache, width) dump <- sg('set_labels', 'TRAIN', label_train) dump <- sg('new_regression', 'LIBSVR') dump <- sg('svr_tube_epsilon', tube_epsilon) dump <- sg('c', C) dump <- sg('train_regression') dump <- sg('set_features', 'TEST', fm_test) result <- sg('classify')
library("sg") size_cache <- 10 C <- 10 tube_epsilon <- 1e-2 width <- 2.1 fm_train <- as.matrix(read.table('../data/fm_train_real.dat')) fm_test <- as.matrix(read.table('../data/fm_test_real.dat')) label_train <- as.real(as.matrix(read.table('../data/label_train_twoclass.dat'))) # SVR Light dosvrlight <- function() { print('SVRLight') dump <- sg('set_features', 'TRAIN', fm_train) dump <- sg('set_kernel', 'GAUSSIAN', 'REAL', size_cache, width) dump <- sg('set_labels', 'TRAIN', label_train) dump <- sg('new_regression', 'SVRLIGHT') dump <- sg('svr_tube_epsilon', tube_epsilon) dump <- sg('c', C) dump <- sg('train_regression') dump <- sg('set_features', 'TEST', fm_test) result <- sg('classify') } try(dosvrlight())