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did more work converting files, having some errors and so pushing cha…
…nges to get errors resolved.
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serialhex committed Aug 21, 2011
1 parent 9e4fe99 commit efa826b
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Showing 136 changed files with 2,030 additions and 1,148 deletions.
@@ -1,7 +1,6 @@
# this was trancekoded by the awesome trancekoder
require 'narray'
# ...and fixifikated by the awesum fixifikator
require 'modshogun'
require 'load'
require 'pp'

parameter_list = [[1,7],[2,8]]
Expand All @@ -14,10 +13,16 @@ def classifier_custom_kernel_modular(c=1,dim=7)
data=rand(dim, dim)
symdata=data*data.T + diag(ones(dim))

kernel=CustomKernel()
# *** kernel=CustomKernel()
kernel=Modshogun::CustomKernel.new
kernel.set_features()
kernel.set_full_kernel_matrix_from_full(data)
labels=Labels(lab)
svm=LibSVM(c, kernel, labels)
# *** labels=Labels(lab)
labels=Modshogun::Labels.new
labels.set_features(lab)
# *** svm=LibSVM(c, kernel, labels)
svm=Modshogun::LibSVM.new
svm.set_features(c, kernel, labels)
svm.train()
predictions =svm.apply()
out=svm.apply().get_labels()
Expand All @@ -26,6 +31,6 @@ def classifier_custom_kernel_modular(c=1,dim=7)
end

if __FILE__ == $0
print 'custom_kernel'
puts 'custom_kernel'
classifier_custom_kernel_modular(*parameter_list[0])
end
@@ -1,7 +1,6 @@
# this was trancekoded by the awesome trancekoder
require 'narray'
# ...and fixifikated by the awesum fixifikator
require 'modshogun'
require 'load'
require 'pp'
import numpy

Expand Down Expand Up @@ -75,7 +74,7 @@ def classifier_domainadaptationsvm_modular(fm_train_dna=traindna,fm_test_dna=tes

#####################################

#print "obtaining DA SVM from previously trained SVM"
# puts "obtaining DA SVM from previously trained SVM"

feats_train2 = StringCharFeatures(fm_train_dna, DNA)
feats_test2 = StringCharFeatures(fm_test_dna, DNA)
Expand All @@ -93,7 +92,7 @@ def classifier_domainadaptationsvm_modular(fm_train_dna=traindna,fm_test_dna=tes

end
if __FILE__ == $0
print 'SVMLight'
puts 'SVMLight'
classifier_domainadaptationsvm_modular(*parameter_list[0])

end
26 changes: 12 additions & 14 deletions examples/undocumented/ruby_modular/classifier_gmnpsvm_modular.rb
@@ -1,7 +1,4 @@
# this was trancekoded by the awesome trancekoder
require 'narray'
require 'modshogun'
require 'load'
require 'pp'

traindat = LoadMatrix.load_numbers('../data/fm_train_real.dat')
Expand All @@ -10,26 +7,27 @@

parameter_list = [[traindat,testdat,label_traindat,2.1,1,1e-5],[traindat,testdat,label_traindat,2.2,1,1e-5]]

def classifier_gmnpsvm_modular(fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,width=2.1,C=1,epsilon=1e-5)
def classifier_gmnpsvm_modular(fm_train_real, fm_test_real, label_train_multiclass, width, c, epsilon)

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)

feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
kernel=Modshogun::GaussianKernel.new(feats_train, feats_train, width)

kernel=GaussianKernel(feats_train, feats_train, width)
labels=Modshogun::Labels.new
labels.set_labels(label_train_multiclass)

labels=Labels(label_train_multiclass)

svm=GMNPSVM(C, kernel, labels)
svm=Modshogun::GMNPSVM.new(c, kernel, labels)
svm.set_epsilon(epsilon)
svm.train(feats_train)
kernel.init(feats_train, feats_test)
out=svm.apply(feats_test).get_labels()
return out,kernel

end
if __FILE__ == $0
print 'GMNPSVM'
classifier_gmnpsvm_modular(*parameter_list[0])

if __FILE__ == $0
puts 'GMNPSVM'
pp classifier_gmnpsvm_modular(*parameter_list[0])
end
25 changes: 17 additions & 8 deletions examples/undocumented/ruby_modular/classifier_gpbtsvm_modular.rb
@@ -1,7 +1,6 @@
# this was trancekoded by the awesome trancekoder
require 'narray'
# ...and fixifikated by the awesum fixifikator
require 'modshogun'
require 'load'
require 'pp'

traindat = LoadMatrix.load_numbers('../data/fm_train_real.dat')
Expand All @@ -14,12 +13,22 @@ def classifier_gpbtsvm_modular(fm_train_real=traindat,fm_test_real=testdat,label



feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
kernel=GaussianKernel(feats_train, feats_train, width)
labels=Labels(label_train_twoclass)
# *** feats_train=RealFeatures(fm_train_real)
feats_train=Modshogun::RealFeatures.new
feats_train.set_features(fm_train_real)
# *** feats_test=RealFeatures(fm_test_real)
feats_test=Modshogun::RealFeatures.new
feats_test.set_features(fm_test_real)
# *** kernel=GaussianKernel(feats_train, feats_train, width)
kernel=Modshogun::GaussianKernel.new
kernel.set_features(feats_train, feats_train, width)
# *** labels=Labels(label_train_twoclass)
labels=Modshogun::Labels.new
labels.set_features(label_train_twoclass)

svm=GPBTSVM(C, kernel, labels)
# *** svm=GPBTSVM(C, kernel, labels)
svm=Modshogun::GPBTSVM.new
svm.set_features(C, kernel, labels)
svm.set_epsilon(epsilon)
svm.train()

Expand All @@ -32,7 +41,7 @@ def classifier_gpbtsvm_modular(fm_train_real=traindat,fm_test_real=testdat,label

end
if __FILE__ == $0
print 'GPBTSVM'
puts 'GPBTSVM'
classifier_gpbtsvm_modular(*parameter_list[0])

end
25 changes: 17 additions & 8 deletions examples/undocumented/ruby_modular/classifier_knn_modular.rb
@@ -1,7 +1,6 @@
# this was trancekoded by the awesome trancekoder
require 'narray'
# ...and fixifikated by the awesum fixifikator
require 'modshogun'
require 'load'
require 'pp'
traindat = LoadMatrix.load_numbers('../data/fm_train_real.dat')
testdat = LoadMatrix.load_numbers('../data/fm_test_real.dat')
Expand All @@ -11,14 +10,24 @@

def classifier_knn_modular(fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat, k=3 )

feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
distance=EuclidianDistance(feats_train, feats_train)
# *** feats_train=RealFeatures(fm_train_real)
feats_train=Modshogun::RealFeatures.new
feats_train.set_features(fm_train_real)
# *** feats_test=RealFeatures(fm_test_real)
feats_test=Modshogun::RealFeatures.new
feats_test.set_features(fm_test_real)
# *** distance=EuclidianDistance(feats_train, feats_train)
distance=Modshogun::EuclidianDistance.new
distance.set_features(feats_train, feats_train)


labels=Labels(label_train_multiclass)
# *** labels=Labels(label_train_multiclass)
labels=Modshogun::Labels.new
labels.set_features(label_train_multiclass)

knn=KNN(k, distance, labels)
# *** knn=KNN(k, distance, labels)
knn=Modshogun::KNN.new
knn.set_features(k, distance, labels)
knn_train = knn.train()
output=knn.apply(feats_test).get_labels()
multiple_k=knn.classify_for_multiple_k()
Expand All @@ -27,7 +36,7 @@ def classifier_knn_modular(fm_train_real=traindat,fm_test_real=testdat,label_tra

end
if __FILE__ == $0
print 'KNN'
puts 'KNN'
classifier_knn_modular(*parameter_list[0])

end
25 changes: 17 additions & 8 deletions examples/undocumented/ruby_modular/classifier_larank_modular.rb
@@ -1,7 +1,6 @@
# this was trancekoded by the awesome trancekoder
require 'narray'
# ...and fixifikated by the awesum fixifikator
require 'modshogun'
require 'load'
require 'pp'

traindat = LoadMatrix.load_numbers('../data/fm_train_real.dat')
Expand All @@ -14,15 +13,25 @@ def classifier_larank_modular(fm_train_real=traindat,fm_test_real=testdat,label_

Math_init_random(17)

feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
# *** feats_train=RealFeatures(fm_train_real)
feats_train=Modshogun::RealFeatures.new
feats_train.set_features(fm_train_real)
# *** feats_test=RealFeatures(fm_test_real)
feats_test=Modshogun::RealFeatures.new
feats_test.set_features(fm_test_real)
width=2.1
kernel=GaussianKernel(feats_train, feats_train, width)
# *** kernel=GaussianKernel(feats_train, feats_train, width)
kernel=Modshogun::GaussianKernel.new
kernel.set_features(feats_train, feats_train, width)

epsilon=1e-5
labels=Labels(label_train_multiclass)
# *** labels=Labels(label_train_multiclass)
labels=Modshogun::Labels.new
labels.set_features(label_train_multiclass)

svm=LaRank(C, kernel, labels)
# *** svm=LaRank(C, kernel, labels)
svm=Modshogun::LaRank.new
svm.set_features(C, kernel, labels)
#svm.set_tau(1e-3)
svm.set_batch_mode(False)
#svm.io.enable_progress()
Expand All @@ -36,7 +45,7 @@ def classifier_larank_modular(fm_train_real=traindat,fm_test_real=testdat,label_

end
if __FILE__ == $0
print 'LaRank'
puts 'LaRank'
classifier_larank_modular(*parameter_list[0])


Expand Down
21 changes: 14 additions & 7 deletions examples/undocumented/ruby_modular/classifier_lda_modular.rb
@@ -1,7 +1,6 @@
# this was trancekoded by the awesome trancekoder
require 'narray'
# ...and fixifikated by the awesum fixifikator
require 'modshogun'
require 'load'
require 'pp'

traindat = LoadMatrix.load_numbers('../data/fm_train_real.dat')
Expand All @@ -12,12 +11,20 @@

def classifier_lda_modular(fm_train_real=traindat,fm_test_real=testdat,label_train_twoclass=label_traindat,gamma=3,num_threads=1)

feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
# *** feats_train=RealFeatures(fm_train_real)
feats_train=Modshogun::RealFeatures.new
feats_train.set_features(fm_train_real)
# *** feats_test=RealFeatures(fm_test_real)
feats_test=Modshogun::RealFeatures.new
feats_test.set_features(fm_test_real)

labels=Labels(label_train_twoclass)
# *** labels=Labels(label_train_twoclass)
labels=Modshogun::Labels.new
labels.set_features(label_train_twoclass)

lda=LDA(gamma, feats_train, labels)
# *** lda=LDA(gamma, feats_train, labels)
lda=Modshogun::LDA.new
lda.set_features(gamma, feats_train, labels)
lda.train()

lda.get_bias()
Expand All @@ -29,7 +36,7 @@ def classifier_lda_modular(fm_train_real=traindat,fm_test_real=testdat,label_tra

end
if __FILE__ == $0
print 'LDA'
puts 'LDA'
classifier_lda_modular(*parameter_list[0])

end
23 changes: 15 additions & 8 deletions examples/undocumented/ruby_modular/classifier_liblinear_modular.rb
@@ -1,7 +1,6 @@
# this was trancekoded by the awesome trancekoder
require 'narray'
# ...and fixifikated by the awesum fixifikator
require 'modshogun'
require 'load'
require 'pp'

traindat = LoadMatrix.load_numbers('../data/fm_train_real.dat')
Expand All @@ -15,11 +14,19 @@ def classifier_liblinear_modular(fm_train_real, fm_test_real,

Math_init_random(17)

feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
labels=Labels(label_train_twoclass)

svm=LibLinear(C, feats_train, labels)
# *** feats_train=RealFeatures(fm_train_real)
feats_train=Modshogun::RealFeatures.new
feats_train.set_features(fm_train_real)
# *** feats_test=RealFeatures(fm_test_real)
feats_test=Modshogun::RealFeatures.new
feats_test.set_features(fm_test_real)
# *** labels=Labels(label_train_twoclass)
labels=Modshogun::Labels.new
labels.set_features(label_train_twoclass)

# *** svm=LibLinear(C, feats_train, labels)
svm=Modshogun::LibLinear.new
svm.set_features(C, feats_train, labels)
svm.set_liblinear_solver_type(L2R_L2LOSS_SVC_DUAL)
svm.set_epsilon(epsilon)
svm.set_bias_enabled(True)
Expand All @@ -35,7 +42,7 @@ def classifier_liblinear_modular(fm_train_real, fm_test_real,

end
if __FILE__ == $0
print 'LibLinear'
puts 'LibLinear'
classifier_liblinear_modular(*parameter_list[0])


Expand Down
27 changes: 18 additions & 9 deletions examples/undocumented/ruby_modular/classifier_libsvm_modular.rb
@@ -1,7 +1,6 @@
# this was trancekoded by the awesome trancekoder
require 'narray'
# ...and fixifikated by the awesum fixifikator
require 'modshogun'
require 'load'
require 'pp'

traindat = LoadMatrix.load_numbers('../data/fm_train_real.dat')
Expand All @@ -12,13 +11,23 @@

def classifier_libsvm_modular(fm_train_real=traindat,fm_test_real=testdat,label_train_twoclass=label_traindat,width=2.1,C=1,epsilon=1e-5)

feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
# *** feats_train=RealFeatures(fm_train_real)
feats_train=Modshogun::RealFeatures.new
feats_train.set_features(fm_train_real)
# *** feats_test=RealFeatures(fm_test_real)
feats_test=Modshogun::RealFeatures.new
feats_test.set_features(fm_test_real)

kernel=GaussianKernel(feats_train, feats_train, width)
labels=Labels(label_train_twoclass)

svm=LibSVM(C, kernel, labels)
# *** kernel=GaussianKernel(feats_train, feats_train, width)
kernel=Modshogun::GaussianKernel.new
kernel.set_features(feats_train, feats_train, width)
# *** labels=Labels(label_train_twoclass)
labels=Modshogun::Labels.new
labels.set_features(label_train_twoclass)

# *** svm=LibSVM(C, kernel, labels)
svm=Modshogun::LibSVM.new
svm.set_features(C, kernel, labels)
svm.set_epsilon(epsilon)
svm.train()

Expand All @@ -32,7 +41,7 @@ def classifier_libsvm_modular(fm_train_real=traindat,fm_test_real=testdat,label_

end
if __FILE__ == $0
print 'LibSVM'
puts 'LibSVM'
classifier_libsvm_modular(*parameter_list[0])

end

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