This page lists ready to run shogun examples for the Ruby Modular interface.

To run the examples issue

ruby name_of_example.rb

Classifier

../examples/documented/ruby_modular/classifier_custom_kernel_modular.rb

# This example shows how to use a custom defined kernel function for training a
# two class Support Vector Machine (SVM) classifier on a randomly generated
# examples. The SVM regularization constant is set to C=1.

require 'modshogun'
require 'load'
require 'narray'
require 'pp'

parameter_list = [[1,7],[2,8]]

def classifier_custom_kernel_modular(c=1,dim=7)

	Modshogun::Math.init_random(c)

	lab = (2*NArray.float(dim).random! - 1).sign
	pp lab
	data= NMatrix.float(dim, dim).random!
	symdata=data*data.transpose + NMatrix.float(dim,dim).unit

	kernel=Modshogun::CustomKernel.new
	kernel.set_full_kernel_matrix_from_full(data)
	labels=Modshogun::BinaryLabels.new(lab)
	svm=Modshogun::LibSVM.new(c, kernel, labels)
	svm.train()
	predictions =svm.apply()
	out=svm.apply().get_labels()
	return svm,out

end

if __FILE__ == $0
	puts 'custom_kernel'
	pp classifier_custom_kernel_modular(*parameter_list[0])
end

../examples/documented/ruby_modular/classifier_libsvm_minimal_modular.rb

# In this example a two-class support vector machine classifier is trained on a
# 2-dimensional randomly generated data set and the trained classifier is used to
# predict labels of test examples. As training algorithm the LIBSVM solver is used
# with SVM regularization parameter C=1 and a Gaussian kernel of width 2.1.
# 
# For more details on LIBSVM solver see http://www.csie.ntu.edu.tw/~cjlin/libsvm/

require 'modshogun'
require 'load'
require 'narray'

@num = 1000
@dist = 1
@width = 2.1
C = 1

puts "generating training data"
traindata_real = gen_rand_ary @num
testdata_real = gen_rand_ary @num

puts "generating labels"
trainlab = gen_ones_vec @num
testlab = gen_ones_vec @num

puts "doing feature stuff"
feats_train = Modshogun::RealFeatures.new
feats_train.set_feature_matrix traindata_real
feats_test = Modshogun::RealFeatures.new
feats_test.set_feature_matrix testdata_real
kernel = Modshogun::GaussianKernel.new feats_train, feats_train, @width

puts "labeling stuff"
labels = Modshogun::BinaryLabels.new
labels.set_labels trainlab
svm = Modshogun::LibSVM.new C, kernel, labels
svm.train

puts "the grand finale"
kernel.init feats_train, feats_test
out = svm.apply.get_labels
testerr = mean out.sign.eql_items? testlab
puts testerr

Clustering

../examples/documented/ruby_modular/clustering_hierarchical_modular.rb

# In this example an agglomerative hierarchical single linkage clustering method
# is used to cluster a given toy data set. Starting with each object being
# assigned to its own cluster clusters are iteratively merged. Here the clusters
# are merged that have the closest (minimum distance, here set via the Euclidean
# distance object) two elements.

require 'modshogun'
require 'pp'
require 'load'

traindat = LoadMatrix.load_numbers('../data/fm_train_real.dat')

parameter_list = [[traindat,3],[traindat,4]]

def clustering_hierarchical_modular(fm_train=traindat,merges=3)

	feats_train=Modshogun::RealFeatures.new
	feats_train.set_feature_matrix(fm_train)
	distance=Modshogun::EuclideanDistance.new(feats_train, feats_train)

	hierarchical=Modshogun::Hierarchical.new(merges, distance)
	hierarchical.train()

	out_distance = hierarchical.get_merge_distances()
	out_cluster = hierarchical.get_cluster_pairs()

	return hierarchical,out_distance,out_cluster

end

if __FILE__ == $0
	puts 'Hierarchical'
	pp clustering_hierarchical_modular(*parameter_list[0])
end

Converter

../examples/documented/ruby_modular/converter_hessianlocallylinearembedding_modular.rb

# In this example toy data is being preprocessed using the Hessian Locally Linear Embedding algorithm
# as described in
# 
# Donoho, D., & Grimes, C. (2003).
# Hessian eigenmaps: new tools for nonlinear dimensionality reduction.
# Proceedings of National Academy of Science (Vol. 100, pp. 5591-5596).

require 'modshogun'
require 'pp'
require 'load'

data = LoadMatrix.load_numbers('../data/fm_train_real.dat')

parameter_list = [[data,10],[data,20]]

def converter_hessianlocallylinearembedding_modular(data,k)

	features = Modshogun::RealFeatures.new
	features.set_feature_matrix(data)

	preprocessor = Modshogun::HessianLocallyLinearEmbedding.new
	preprocessor.set_target_dim(1)
	preprocessor.set_k(k)
	preprocessor.apply(features)

	return features

end

if __FILE__ == $0
	puts 'HessianLocallyLinearEmbedding'
	pp converter_hessianlocallylinearembedding_modular(*parameter_list[0])
end

Distance

../examples/documented/ruby_modular/distance_braycurtis_modular.rb

# An approach as applied below, which shows the processing of input data
# from a file becomes a crucial factor for writing your own sample applications.
# This approach is just one example of what can be done using the distance
# functions provided by shogun.
# 
# First, you need to determine what type your data will be, because this
# will determine the distance function you can use.
# 
# This example loads two stored matrices of real values from different
# files and initializes the matrices to 'RealFeatures'.
# Each column of the matrices corresponds to one data point.
# 
# The distance initialized by two data sets (the same data set as shown in the
# first call) controls the processing of the given data points, where a pairwise
# distance matrix is computed by 'get_distance_matrix'.
# 
# The resulting distance matrix can be reaccessed by 'get_distance_matrix'.
# 
# The method call 'init'* binds the given data sets, where a pairwise distance
# matrix between these two data sets is computed by 'get_distance_matrix'.
# 
# The resulting distance matrix can be reaccessed by 'get_distance_matrix'.
# 
# *Note that the previous computed distance matrix can no longer be
# reaccessed by 'get_distance_matrix'.
# 
# For more details see doc/classshogun_1_1CBrayCurtisDistance.html.
# 
# Obviously, using the Bray Curtis distance is not limited to this showcase
# example.

require 'modshogun'
require 'pp'
require 'load'

traindat = LoadMatrix.load_numbers('../data/fm_train_real.dat')
testdat = LoadMatrix.load_numbers('../data/fm_test_real.dat')

parameter_list = [[traindat,testdat],[traindat,testdat]]

def distance_braycurtis_modular(fm_train_real=traindat, fm_test_real=testdat)

	feats_train=Modshogun::RealFeatures.new
	feats_train.set_feature_matrix(fm_train_real)
	feats_test=Modshogun::RealFeatures.new
	feats_test.set_feature_matrix(fm_test_real)

	distance=Modshogun::BrayCurtisDistance.new(feats_train, feats_train)

	dm_train=distance.get_distance_matrix()
	distance.init(feats_train, feats_test)
	dm_test=distance.get_distance_matrix()

	return distance,dm_train,dm_test
end

if __FILE__ == $0
	puts 'BrayCurtisDistance'
	pp distance_braycurtis_modular(*parameter_list[0])
end

../examples/documented/ruby_modular/distance_canberra_modular.rb

# An approach as applied below, which shows the processing of input data
# from a file becomes a crucial factor for writing your own sample applications.
# This approach is just one example of what can be done using the distance
# functions provided by shogun.
# 
# First, you need to determine what type your data will be, because this
# will determine the distance function you can use.
# 
# This example loads two stored matrices of real values from different
# files and initializes the matrices to 'RealFeatures'.
# Each column of the matrices corresponds to one data point.
# 
# The distance initialized by two data sets (the same data set as shown in the
# first call) controls the processing of the given data points, where a pairwise
# distance (dissimilarity ratio) matrix is computed by 'get_distance_matrix'.
# 
# The resulting distance matrix can be reaccessed by 'get_distance_matrix'.
# 
# The method call 'init'* binds the given data sets, where a pairwise distance
# matrix between these two data sets is computed by 'get_distance_matrix'.
# 
# The resulting distance matrix can be reaccessed by 'get_distance_matrix'.
# 
# *Note that the previous computed distance matrix can no longer be
# reaccessed by 'get_distance_matrix'.
# 
# For more details see doc/classshogun_1_1CCanberraMetric.html.
# 
# Obviously, using the Canberra distance is not limited to this showcase
# example.

require 'modshogun'
require 'pp'
require 'load'

traindat = LoadMatrix.load_numbers('../data/fm_train_real.dat')
testdat = LoadMatrix.load_numbers('../data/fm_test_real.dat')

parameter_list = [[traindat,testdat],[traindat,testdat]]

def distance_canberra_modular(fm_train_real=traindat,fm_test_real=testdat)

	feats_train=Modshogun::RealFeatures.new
	feats_train.set_feature_matrix(fm_train_real)
	feats_test=Modshogun::RealFeatures.new
	feats_test.set_feature_matrix(fm_test_real)

	distance=Modshogun::CanberraMetric.new(feats_train, feats_train)

	dm_train=distance.get_distance_matrix()
	distance.init(feats_train, feats_test)
	dm_test=distance.get_distance_matrix()
	return distance,dm_train,dm_test

end

if __FILE__ == $0
	puts 'CanberaMetric'
	pp distance_canberra_modular(*parameter_list[0])
end

Distribution

../examples/documented/ruby_modular/distribution_histogram_modular.rb

# In this example the Histogram algorithm object computes a histogram over all
# 16bit unsigned integers in the features.

require 'modshogun'
require 'pp'
require 'load'

traindna = LoadMatrix.load_dna('../data/fm_train_dna.dat')

parameter_list = [[traindna, 3, 0, false], [traindna, 4, 0, false]]

def distribution_histogram_modular(fm_dna=traindna, order=3, gap=0, reverse=false)

	charfeat=Modshogun::StringCharFeatures.new(Modshogun::DNA)
	charfeat.set_features(fm_dna)
	feats=Modshogun::StringWordFeatures.new(charfeat.get_alphabet())
	feats.obtain_from_char(charfeat, order-1, order, gap, reverse)

	histo=Modshogun::Histogram.new
	histo.set_features(feats)
	histo.train()

	histo.get_histogram()

	num_examples=feats.get_num_vectors()
	num_param=histo.get_num_model_parameters()
	#for i in xrange(num_examples):
	#	for j in xrange(num_param):
	#		histo.get_log_derivative(j, i)

	out_likelihood = histo.get_log_likelihood()
	out_sample = histo.get_log_likelihood_sample()
	return histo,out_sample,out_likelihood

end

###########################################################################
# call functions
###########################################################################

if __FILE__ == $0
	puts 'Histogram'
	pp distribution_histogram_modular(*parameter_list[0])
end

Features

../examples/documented/ruby_modular/features_io_modular.rb

# This example shows how to read and write plain ascii files, binary files and
# hdf5 datasets.
# 
# For ascii files it shows how to obtain shogun's RealFeatures
# (a simple feature matrix of doubles with 1 column == 1 example, nr_columns ==
#  number of examples) and also sparse features in SVM light format.
# 
# Binary files use some custom native format and datasets can be read/written
# from/to hdf5 files with arbitrary group / path.

require 'load'
require 'modshogun'
require 'pp'

data=LoadMatrix.load_numbers('../data/fm_train_real.dat')
label=LoadMatrix.load_numbers('../data/label_train_twoclass.dat')

parameter_list=[[data,label]]

def features_io_modular(fm_train_real, label_train_twoclass)

	feats=Modshogun::SparseRealFeatures.new
	feats.set_full_feature_matrix(fm_train_real)
	feats2=Modshogun::SparseRealFeatures.new

	f=Modshogun::BinaryFile.new("fm_train_sparsereal.bin","w")
	feats.save(f)
	f.close()

	f=Modshogun::LibSVMFile.new("fm_train_sparsereal.ascii","w")
	feats.save(f)
	f.close()

	f=Modshogun::BinaryFile.new("fm_train_sparsereal.bin", "r")
	feats2.load(f)
	f.close()

	f=Modshogun::LibSVMFile.new("fm_train_sparsereal.ascii")
	feats2.load(f)
	f.close()

	feats=Modshogun::RealFeatures.new
	feats.set_feature_matrix(fm_train_real)
	feats2=Modshogun::RealFeatures.new

	f=Modshogun::BinaryFile.new("fm_train_real.bin","w")
	feats.save(f)
	f.close()

	f=Modshogun::HDF5File.new("fm_train_real.h5","w", "/data/doubles")
	feats.save(f)
	f.close()

	f=Modshogun::CSVFile.new("fm_train_real.ascii","w")
	feats.save(f)
	f.close()

	f=Modshogun::BinaryFile.new("fm_train_real.bin")
	feats2.load(f)
	f.close()

	f=Modshogun::CSVFile.new("fm_train_real.ascii")
	feats2.load(f)
	f.close()

	lab=Modshogun::MulticlassLabels.new([0.0,1.0,2.0,3.0])
	lab2=Modshogun::MulticlassLabels.new

	f=Modshogun::CSVFile.new("label_train_twoclass.ascii","w")
	lab.save(f)
	f.close()

	f=Modshogun::BinaryFile.new("label_train_twoclass.bin","w")
	lab.save(f)
	f.close()

	f=Modshogun::HDF5File.new("label_train_real.h5","w", "/data/labels")
	lab.save(f)
	f.close()

	f=Modshogun::CSVFile.new("label_train_twoclass.ascii")
	lab2.load(f)
	f.close()

	f=Modshogun::BinaryFile.new("label_train_twoclass.bin")
	lab2.load(f)
	f.close()

	f=Modshogun::HDF5File.new("fm_train_real.h5","r", "/data/doubles")
	feats2.load(f)
	f.close()

	f=Modshogun::HDF5File.new("label_train_real.h5","r", "/data/labels")
	lab2.load(f)
	f.close()
	#pp lab.get_labels()

	##clean up
	#import os
	#for f in ['fm_train_sparsereal.bin','fm_train_sparsereal.ascii',
	#		'fm_train_real.bin','fm_train_real.h5','fm_train_real.ascii',
	#		'label_train_real.h5', 'label_train_twoclass.ascii','label_train_twoclass.bin']:
	#	os.unlink(f)
	#end

	return feats, feats2, lab, lab2
end

if __FILE__ == $0
	puts 'Features IO'
	pp features_io_modular(*parameter_list[0])
end

../examples/documented/ruby_modular/features_string_char_modular.rb

# This example demonstrates how to encode ASCII-strings (255 symbols) in shogun.

require 'modshogun'
require 'pp'

strings=['hey','guys','i','am','a','string']

parameter_list=[strings]

def features_string_char_modular(strings)

	#create string features
	f=Modshogun::StringCharFeatures.new(strings, Modshogun::RAWBYTE)

	#and output several stats
	#puts "max string length", f.get_max_vector_length
	#puts "number of strings", f.get_num_vectors
	#puts "length of first string", f.get_vector_length(0)
	#puts "string[5]", f.get_feature_vector(5)
	#puts "strings", f.get_features

	#replace string 0
	f.set_feature_vector(['t','e','s','t'], 0)

	return f.get_features, f

end

if __FILE__ == $0
	puts 'StringCharFeatures'
	pp features_string_char_modular(*parameter_list)
end

Kernel

../examples/documented/ruby_modular/kernel_anova_modular.rb

# In this example the ANOVA kernel is being computed for toy data.

require 'modshogun'
require 'pp'
require 'load'
###########################################################################
# anova kernel
###########################################################################

traindat = LoadMatrix.load_numbers('../data/fm_train_real.dat')
testdat = LoadMatrix.load_numbers('../data/fm_test_real.dat')
parameter_list = [[traindat,testdat,2,10], [traindat,testdat,5,10]]

def kernel_anova_modular(fm_train_real=traindat,fm_test_real=testdat,cardinality=2, size_cache=10)

	feats_train=Modshogun::RealFeatures.new
	feats_train.set_feature_matrix(fm_train_real)
	feats_test=Modshogun::RealFeatures.new
	feats_test.set_feature_matrix(fm_test_real)

	kernel=Modshogun::ANOVAKernel.new(feats_train, feats_train, cardinality, size_cache)

	for i in 0..feats_train.get_num_vectors
		for j in 0..feats_train.get_num_vectors
			k1 = kernel.compute_rec1(i,j)
			k2 = kernel.compute_rec2(i,j)
			if (k1-k2).abs > 1e-10
				puts "|#{k1}|#{k2}|"
			end
		end
	end

	km_train=kernel.get_kernel_matrix()
	kernel.init(feats_train, feats_test)
	km_test=kernel.get_kernel_matrix()
	return km_train, km_test, kernel
end

if __FILE__ == $0
	puts 'ANOVA'
	pp kernel_anova_modular(*parameter_list[0])
end

../examples/documented/ruby_modular/kernel_auc_modular.rb

# This example demonstrates the use of the AUC Kernel, which
# can be used to maximize AUC instead of margin in SVMs.

require 'load'
require 'modshogun'
require 'pp'

traindat = LoadMatrix.load_numbers('../data/fm_train_real.dat')
testdat = LoadMatrix.load_labels('../data/label_train_twoclass.dat')
parameter_list = [[traindat,testdat,1.7], [traindat,testdat,1.6]]


def kernel_auc_modular(fm_train_real=traindat,label_train_real=testdat,width=1.7)

# ***	feats_train=RealFeatures(fm_train_real)
	feats_train=Modshogun::RealFeatures.new
	feats_train.set_feature_matrix(fm_train_real)

# ***	subkernel=GaussianKernel(feats_train, feats_train, width)
	subkernel=Modshogun::GaussianKernel.new(feats_train, feats_train, width)

# ***	kernel=AUCKernel(0, subkernel)
	kernel=Modshogun::AUCKernel.new(0, subkernel)
	kernel.setup_auc_maximization( Modshogun::BinaryLabels.new(label_train_real) )
	km_train=kernel.get_kernel_matrix()
	return kernel
end

if __FILE__ == $0
	puts 'AUC'
	pp kernel_auc_modular(*parameter_list[0])
end

../examples/documented/ruby_modular/kernel_cauchy_modular.rb

# In this example the Cauchy kernel is being computed for toy data.

require 'modshogun'
require 'pp'
require 'load'

traindat = LoadMatrix.load_numbers('../data/fm_train_real.dat')
testdat = LoadMatrix.load_numbers('../data/fm_test_real.dat')

parameter_list=[[traindat,testdat, 1.0],[traindat,testdat, 10.0]]

def kernel_cauchy_modular(fm_train_real=traindat,fm_test_real=testdat, sigma=1.0)

	feats_train=Modshogun::RealFeatures.new
	feats_train.set_feature_matrix(fm_train_real)
	feats_test=Modshogun::RealFeatures.new
	feats_test.set_feature_matrix(fm_test_real)

	distance=Modshogun::EuclideanDistance.new(feats_train, feats_train)

	kernel=Modshogun::CauchyKernel.new(feats_train, feats_train, sigma, distance)
	km_train=kernel.get_kernel_matrix()

	kernel.init(feats_train, feats_test)
	km_test=kernel.get_kernel_matrix()
	return km_train,km_test,kernel
end

if __FILE__ == $0
	puts 'Cauchy'
	pp kernel_cauchy_modular(*parameter_list[0])
end

../examples/documented/ruby_modular/kernel_chi2_modular.rb

# This is an example for the initialization of the chi2-kernel on real data, where
# each column of the matrices corresponds to one training/test example.

require 'modshogun'
require 'pp'
require 'load'
###########################################################################
# chi2 kernel
###########################################################################

traindat = LoadMatrix.load_numbers('../data/fm_train_real.dat')
testdat = LoadMatrix.load_numbers('../data/fm_test_real.dat')
parameter_list = [[traindat,testdat,1.4,10], [traindat,testdat,1.5,10]]

def kernel_chi2_modular(fm_train_real=traindat,fm_test_real=testdat,width=1.4, size_cache=10)

	feats_train=Modshogun::RealFeatures.new
	feats_train.set_feature_matrix(fm_train_real)
	feats_test=Modshogun::RealFeatures.new
	feats_test.set_feature_matrix(fm_test_real)


	kernel=Modshogun::Chi2Kernel.new(feats_train, feats_train, width, size_cache)

	km_train=kernel.get_kernel_matrix()
	kernel.init(feats_train, feats_test)
	km_test=kernel.get_kernel_matrix()
	return km_train,km_test,kernel
end

if __FILE__ == $0
	puts 'Chi2'
	pp kernel_chi2_modular(*parameter_list[0])
end

../examples/documented/ruby_modular/kernel_circular_modular.rb

# In this example the circular kernel is being computed for toy data.

require 'modshogun'
require 'pp'
require 'load'

traindat = LoadMatrix.load_numbers('../data/fm_train_real.dat')
testdat = LoadMatrix.load_numbers('../data/fm_test_real.dat')

parameter_list=[[traindat,testdat, 1.0],[traindat,testdat, 5.0]]

def kernel_circular_modular(fm_train_real=traindat,fm_test_real=testdat, sigma=1.0)

	feats_train=Modshogun::RealFeatures.new
	feats_train.set_feature_matrix(fm_train_real)
	feats_test=Modshogun::RealFeatures.new
	feats_test.set_feature_matrix(fm_test_real)

	distance=Modshogun::EuclideanDistance.new(feats_train, feats_train)

	kernel=Modshogun::CircularKernel.new(feats_train, feats_train, sigma, distance)
	km_train=kernel.get_kernel_matrix()

	kernel.init(feats_train, feats_test)
	km_test=kernel.get_kernel_matrix()
	return km_train,km_test,kernel
end

if __FILE__ == $0
	puts 'Circular'
	pp kernel_circular_modular(*parameter_list[0])
end

../examples/documented/ruby_modular/kernel_combined_custom_poly_modular.rb

# In this example the combined kernel of custom kernel and poly kernel is being computed for toy data.

require 'modshogun'
require 'pp'
require 'load'

traindat = LoadMatrix.load_numbers('../data/fm_train_real.dat')
testdat = LoadMatrix.load_numbers('../data/fm_test_real.dat')
label_traindat = LoadMatrix.load_labels('../data/label_train_twoclass.dat')
parameter_list= [[traindat,testdat,label_traindat],[traindat,testdat,label_traindat]]


def kernel_combined_custom_poly_modular(fm_train_real = traindat,fm_test_real = testdat,fm_label_twoclass=label_traindat)

    kernel = Modshogun::CombinedKernel.new
    feats_train = Modshogun::CombinedFeatures.new

    tfeats = Modshogun::RealFeatures.new
    tfeats.set_feature_matrix(fm_train_real)
    tkernel = Modshogun::PolyKernel.new(10,3)
    tkernel.init(tfeats, tfeats)
    k = tkernel.get_kernel_matrix()
    f = Modshogun::CustomKernel.new
    f.set_full_kernel_matrix_from_full(k)
    kernel.append_kernel(f)

    subkfeats_train = Modshogun::RealFeatures.new
    subkfeats_train.set_feature_matrix(fm_train_real)
    feats_train.append_feature_obj(subkfeats_train)
    subkernel = Modshogun::PolyKernel.new(10,2)
    kernel.append_kernel(subkernel)

    kernel.init(feats_train, feats_train)

    labels = Modshogun::BinaryLabels.new(fm_label_twoclass)
    svm = Modshogun::LibSVM.new(1.0, kernel, labels)
    svm.train()

    kernel = Modshogun::CombinedKernel.new
    feats_pred = Modshogun::CombinedFeatures.new

    pfeats = Modshogun::RealFeatures.new
    pfeats.set_feature_matrix(fm_test_real)
    tkernel = Modshogun::PolyKernel.new(10,3)
    tkernel.init(tfeats, pfeats)
    k = tkernel.get_kernel_matrix()
    f = Modshogun::CustomKernel.new
    f.set_full_kernel_matrix_from_full(k)
    kernel.append_kernel(f)

    subkfeats_test = Modshogun::RealFeatures.new
    subkfeats_test.set_feature_matrix(fm_test_real)
    feats_pred.append_feature_obj(subkfeats_test)
    subkernel = Modshogun::PolyKernel.new(10, 2)
    kernel.append_kernel(subkernel)
    kernel.init(feats_train, feats_pred)

    svm.set_kernel(kernel)
    svm.apply()
    km_train=kernel.get_kernel_matrix()
    return km_train,kernel
end

if __FILE__ == $0
    puts 'Combined Custom Poly Modular'
    pp kernel_combined_custom_poly_modular(*parameter_list[0])
end

../examples/documented/ruby_modular/kernel_gaussian_modular.rb

# The well known Gaussian kernel (swiss army knife for SVMs) on dense real valued features.

require 'narray'
require 'modshogun'
require 'load'
require 'pp'

traindat = LoadMatrix.load_numbers('../data/fm_train_real.dat')
testdat = LoadMatrix.load_numbers('../data/fm_test_real.dat')

parameter_list=[[traindat,testdat, 1.3],[traindat,testdat, 1.4]]

def kernel_gaussian_modular(fm_train_real=traindat,fm_test_real=testdat, width=1.3)
  pp fm_train_real
	feats_train=Modshogun::RealFeatures.new
	feats_train.set_feature_matrix fm_train_real
	feats_test=Modshogun::RealFeatures.new
	feats_test.set_feature_matrix fm_test_real

	kernel=Modshogun::GaussianKernel.new feats_train, feats_train, width
	km_train=kernel.get_kernel_matrix()

	kernel.init(feats_train, feats_test)
	km_test=kernel.get_kernel_matrix()
	pp km_train
	return km_train,km_test,kernel
end

if __FILE__ == $0 then
	puts 'Gaussian'
	kernel_gaussian_modular(*parameter_list[0])
end

Modelselection

../examples/documented/ruby_modular/modelselection_parameter_tree_modular.rb

# In this example a complex model parameters selection tree
# is being constructed

require 'modshogun'
require 'load'
require 'pp'

#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# Written (W) 2011 Heiko Strathmann
# Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
# Trancekoded (T) 2011 Justin Patera aka serialhex

def modelselection_parameter_tree_modular()

	root=Modshogun::ModelSelectionParameters.new

	combinations=root.get_combinations()
	combinations.get_num_elements()

	c=Modshogun::ModelSelectionParameters.new('C')
	root.append_child(c)
	c.build_values(1, 11, Modshogun::R_EXP)

	power_kernel=Modshogun::PowerKernel.new
	param_power_kernel=Modshogun::ModelSelectionParameters.new('kernel', power_kernel)
	root.append_child(param_power_kernel)

	param_power_kernel_degree=Modshogun::ModelSelectionParameters.new('degree')
	param_power_kernel_degree.build_values(1, 1, Modshogun::R_EXP)
	param_power_kernel.append_child(param_power_kernel_degree)

	metric1=Modshogun::MinkowskiMetric.new(10)
	param_power_kernel_metric1=Modshogun::ModelSelectionParameters.new('distance', metric1)

	param_power_kernel.append_child(param_power_kernel_metric1)

	param_power_kernel_metric1_k=Modshogun::ModelSelectionParameters.new('k')
	param_power_kernel_metric1_k.build_values(1, 12, Modshogun::R_LINEAR)
	param_power_kernel_metric1.append_child(param_power_kernel_metric1_k)

	gaussian_kernel=Modshogun::GaussianKernel.new
	param_gaussian_kernel=Modshogun::ModelSelectionParameters.new('kernel', gaussian_kernel)

	root.append_child(param_gaussian_kernel)

	param_gaussian_kernel_width=Modshogun::ModelSelectionParameters.new('width')
	param_gaussian_kernel_width.build_values(1, 2, Modshogun::R_EXP)
	param_gaussian_kernel.append_child(param_gaussian_kernel_width)

	ds_kernel=Modshogun::DistantSegmentsKernel.new
	param_ds_kernel=Modshogun::ModelSelectionParameters.new('kernel', ds_kernel)

	root.append_child(param_ds_kernel)

	param_ds_kernel_delta=Modshogun::ModelSelectionParameters.new('delta')
	param_ds_kernel_delta.build_values(1, 2, Modshogun::R_EXP)
	param_ds_kernel.append_child(param_ds_kernel_delta)

	param_ds_kernel_theta=Modshogun::ModelSelectionParameters.new('theta')
	param_ds_kernel_theta.build_values(1, 2, Modshogun::R_EXP)
	param_ds_kernel.append_child(param_ds_kernel_theta)

	#root.print_tree()
	combinations=root.get_combinations()
	#combinations.get_num_elements.times do |i|
	#	combinations.get_element(i).print_tree()
	#end

	return

end

if __FILE__ == $0
	puts 'ParameterTree'
	pp modelselection_parameter_tree_modular()
end

Regression

../examples/documented/ruby_modular/regression_krr_modular.rb

# In this example a kernelized version of ridge regression (KRR) is trained on a
# real-valued data set. The KRR is trained with regularization parameter tau=1e-6
# and a gaussian kernel with width=0.8. The labels of both the train and the test
# data can be fetched via krr.classify().get_labels().

require 'modshogun'
require 'pp'
require 'load'
###########################################################################
# kernel ridge regression
###########################################################################

traindat = LoadMatrix.load_numbers('../data/fm_train_real.dat')
testdat = LoadMatrix.load_numbers('../data/fm_test_real.dat')
label_traindat = LoadMatrix.load_labels('../data/label_train_twoclass.dat')


parameter_list = [[traindat,testdat,label_traindat,0.8,1e-6],[traindat,testdat,label_traindat,0.9,1e-7]]

def regression_krr_modular(fm_train=traindat,fm_test=testdat,label_train=label_traindat,width=0.8,tau=1e-6)


	feats_train=Modshogun::RealFeatures.new
	feats_train.set_feature_matrix(fm_train)
	feats_test=Modshogun::RealFeatures.new
	feats_test.set_feature_matrix(fm_test)

	kernel=Modshogun::GaussianKernel.new(feats_train, feats_train, width)

	labels=Modshogun::RegressionLabels.new(label_train)

	krr=Modshogun::KernelRidgeRegression.new(tau, kernel, labels)
	krr.train(feats_train)

	kernel.init(feats_train, feats_test)
	out = krr.apply().get_labels()
	return out,kernel,krr

end

# equivialent shorter version
## probably dosn't work yet
def krr_short()
	puts 'KRR_short'

	width=0.8; tau=1e-6
# ***	krr=KernelRidgeRegression(tau, GaussianKernel(0, width), RegressionLabels(label_train))
	krr=Modshogun::KernelRidgeRegression.new(tau, GaussianKernel(0, width), RegressionLabels(label_train))
	#krr.set_features(tau, GaussianKernel(0, width), RegressionLabels(label_train))
	krr.train(RealFeatures(fm_train))
	out = Modshogun::LabelsFactory.to_regression(krr.apply(RealFeatures(fm_test)).get_labels())

	return krr,out

end

if __FILE__ == $0
	puts 'KernelRidgeRegression'
	pp regression_krr_modular(*parameter_list[0])
end

../examples/documented/ruby_modular/regression_libsvr_modular.rb

# In this example a support vector regression algorithm is trained on a
# real-valued toy data set. The underlying library used for the SVR training is
# LIBSVM. Both the epsilon-SVR and the nu-SVR formulation are used.
# Data is a noisy sine wave on which a Gaussian kernel is used.
# 
# For more details on LIBSVM solver see http://www.csie.ntu.edu.tw/~cjlin/libsvm/ .

require 'modshogun'
require 'pp'
require 'load'

traindat = LoadMatrix.load_numbers('../data/fm_train_real.dat')
testdat = LoadMatrix.load_numbers('../data/fm_test_real.dat')
label_traindat = LoadMatrix.load_labels('../data/label_train_twoclass.dat')


parameter_list = [[traindat,testdat,label_traindat,2.1,1,1e-5,1e-2], [traindat,testdat,label_traindat,2.1,1,1e-5,1e-2]]


def regression_libsvr_modular(fm_train=traindat,fm_test=testdat,label_train=label_traindat,width=2.1,c=1,epsilon=1e-5,tube_epsilon=1e-2)


	feats_train=Modshogun::RealFeatures.new
	feats_train.set_feature_matrix(fm_train)
	feats_test=Modshogun::RealFeatures.new
	feats_test.set_feature_matrix(fm_test)

	kernel=Modshogun::GaussianKernel.new(feats_train, feats_train, width)
	labels=Modshogun::RegressionLabels.new(label_train)

	svr=Modshogun::LibSVR.new(c, tube_epsilon, kernel, labels)
	svr.set_epsilon(epsilon)
	svr.train()

	kernel.init(feats_train, feats_test)
	out1=svr.apply().get_labels()
	out2=svr.apply(feats_test).get_labels()

	return out1,out2,kernel

end

if __FILE__ == $0
	puts 'LibSVR'
	pp regression_libsvr_modular(*parameter_list[0])
end