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assume make install was called before running lua examples / check.sh
and remove shogun.lua / always use modshogun.Label etc
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Soeren Sonnenburg committed Aug 23, 2011
1 parent a5262da commit 59a6f41
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Showing 19 changed files with 92 additions and 262 deletions.
7 changes: 0 additions & 7 deletions examples/undocumented/lua_modular/MatrixTest.lua

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6 changes: 0 additions & 6 deletions examples/undocumented/lua_modular/VectorTest.lua

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3 changes: 1 addition & 2 deletions examples/undocumented/lua_modular/check.sh
Expand Up @@ -2,8 +2,7 @@

rm -f error.log

export LUA_PATH=../../../src/interfaces/lua_modular/?.lua\;?.lua
export LUA_CPATH=../../../src/interfaces/lua_modular/?.so
#export LUA_CPATH=../../../src/interfaces/lua_modular/?.so

for e in *.lua
do
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@@ -1,4 +1,4 @@
require 'shogun'
require 'modshogun'
require 'load'

traindat = load_numbers('../data/fm_train_real.dat')
Expand All @@ -9,12 +9,12 @@ parameter_list = {{traindat,testdat,label_traindat,1.,1000,1},{traindat,testdat,

function classifier_averaged_perceptron_modular (fm_train_real,fm_test_real,label_train_twoclass,learn_rate,max_iter,num_threads)

feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
feats_train=modshogun.RealFeatures(fm_train_real)
feats_test=modshogun.RealFeatures(fm_test_real)

labels=Labels(label_train_twoclass)
labels=modshogun.Labels(label_train_twoclass)

perceptron=AveragedPerceptron(feats_train, labels)
perceptron=modshogun.AveragedPerceptron(feats_train, labels)
perceptron:set_learn_rate(learn_rate)
perceptron:set_max_iter(max_iter)

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@@ -1,4 +1,4 @@
require 'shogun'
require 'modshogun'
require 'load'

function concatenate(...)
Expand Down Expand Up @@ -53,12 +53,12 @@ for i = 1, num do
testlab[i + num] = 1
end

feats_train=RealFeatures(traindata_real)
feats_test=RealFeatures(testdata_real)
kernel=GaussianKernel(feats_train, feats_train, width)
feats_train=modshogun.RealFeatures(traindata_real)
feats_test=modshogun.RealFeatures(testdata_real)
kernel=modshogun.GaussianKernel(feats_train, feats_train, width)

labels=Labels(trainlab)
svm=LibSVM(C, kernel, labels)
labels=modshogun.Labels(trainlab)
svm=modshogun.LibSVM(C, kernel, labels)
svm:train()

kernel:init(feats_train, feats_test)
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@@ -1,4 +1,4 @@
require 'shogun'
require 'modshogun'
require 'load'

traindat = load_numbers('../data/fm_train_real.dat')
Expand All @@ -8,10 +8,10 @@ parameter_list = {{traindat,testdat},{traindat,testdat}}

function distance_braycurtis_modular (fm_train_real,fm_test_real)

feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
feats_train=modshogun.RealFeatures(fm_train_real)
feats_test=modshogun.RealFeatures(fm_test_real)

distance=BrayCurtisDistance(feats_train, feats_train)
distance=modshogun.BrayCurtisDistance(feats_train, feats_train)

dm_train=distance:get_distance_matrix()
distance:init(feats_train, feats_test)
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@@ -1,4 +1,4 @@
require 'shogun'
require 'modshogun'
require 'load'

ground_truth = load_labels('../data/label_train_twoclass.dat')
Expand All @@ -12,37 +12,37 @@ parameter_list = {{ground_truth,predicted}}

function evaluation_contingencytableevaluation_modular(ground_truth, predicted)

ground_truth_labels = Labels(ground_truth)
predicted_labels = Labels(predicted)
ground_truth_labels = modshogun.Labels(ground_truth)
predicted_labels = modshogun.Labels(predicted)

base_evaluator = ContingencyTableEvaluation()
base_evaluator = modshogun.ContingencyTableEvaluation()
base_evaluator:evaluate(predicted_labels,ground_truth_labels)

evaluator = AccuracyMeasure()
evaluator = modshogun.AccuracyMeasure()
accuracy = evaluator:evaluate(predicted_labels,ground_truth_labels)

evaluator = ErrorRateMeasure()
evaluator = modshogun.ErrorRateMeasure()
errorrate = evaluator:evaluate(predicted_labels,ground_truth_labels)

evaluator = BALMeasure()
evaluator = modshogun.BALMeasure()
bal = evaluator:evaluate(predicted_labels,ground_truth_labels)

evaluator = WRACCMeasure()
evaluator = modshogun.WRACCMeasure()
wracc = evaluator:evaluate(predicted_labels,ground_truth_labels)

evaluator = F1Measure()
evaluator = modshogun.F1Measure()
f1 = evaluator:evaluate(predicted_labels,ground_truth_labels)

evaluator = CrossCorrelationMeasure()
evaluator = modshogun.CrossCorrelationMeasure()
crosscorrelation = evaluator:evaluate(predicted_labels,ground_truth_labels)

evaluator = RecallMeasure()
evaluator = modshogun.RecallMeasure()
recall = evaluator:evaluate(predicted_labels,ground_truth_labels)

evaluator = PrecisionMeasure()
evaluator = modshogun.PrecisionMeasure()
precision = evaluator:evaluate(predicted_labels,ground_truth_labels)

evaluator = SpecificityMeasure()
evaluator = modshogun.SpecificityMeasure()
specificity = evaluator:evaluate(predicted_labels,ground_truth_labels)

return accuracy, errorrate, bal, wracc, f1, crosscorrelation, recall, precision, specificity
Expand Down
@@ -1,11 +1,11 @@
require 'shogun'
require 'modshogun'

matrix = {{1,2,3},{4,0,0},{0,0,0},{0,5,0},{0,0,6},{9,9,9}}

parameter_list = {{matrix}}

function features_simple_real_modular(A)
a=RealFeatures(A)
a=modshogun.RealFeatures(A)
a:set_feature_vector({1,4,0,0,0,9}, 0)

a_out = a:get_feature_matrix()
Expand Down
@@ -1,12 +1,12 @@
require 'shogun'
require 'modshogun'
require 'load'

strings = {'hey','guys','i','am','a','string'}
parameter_list={{strings}}

function features_string_char_modular(strings)
for k, v in pairs(strings) do print(v) end
f=StringCharFeatures(strings, RAWBYTE)
f=modshogun.StringCharFeatures(strings, modshogun.RAWBYTE)

print("max string length " ..f:get_max_vector_length())
print("number of strings " .. f:get_num_vectors())
Expand Down
@@ -1,31 +1,31 @@
require 'shogun'
require 'modshogun'
require 'load'

traindat = load_dna('../data/fm_train_dna.dat')
testdat = load_dna('../data/fm_test_dna.dat')
parameter_list = {{traindat,testdat,3,0,false},{traindat,testdat,4,0,false}}

function kernel_comm_ulong_string_modular (fm_train_dna,fm_test_dna, order, gap, reverse)
charfeat=StringCharFeatures(DNA)
charfeat=modshogun.StringCharFeatures(modshogun.DNA)
charfeat:set_features(fm_train_dna)
feats_train=StringUlongFeatures(charfeat:get_alphabet())
feats_train=modshogun.StringUlongFeatures(charfeat:get_alphabet())
feats_train:obtain_from_char(charfeat, order-1, order, gap, reverse)
preproc=SortUlongString()
preproc=modshogun.SortUlongString()
preproc:init(feats_train)
feats_train:add_preprocessor(preproc)
feats_train:apply_preprocessor()


charfeat=StringCharFeatures(DNA)
charfeat=modshogun.StringCharFeatures(modshogun.DNA)
charfeat:set_features(fm_test_dna)
feats_test=StringUlongFeatures(charfeat:get_alphabet())
feats_test=modshogun.StringUlongFeatures(charfeat:get_alphabet())
feats_test:obtain_from_char(charfeat, order-1, order, gap, reverse)
feats_test:add_preprocessor(preproc)
feats_test:apply_preprocessor()

use_sign=false

kernel=CommUlongStringKernel(feats_train, feats_train, use_sign)
kernel=modshogun.CommUlongStringKernel(feats_train, feats_train, use_sign)

km_train=kernel:get_kernel_matrix()
kernel:init(feats_train, feats_test)
Expand Down
@@ -1,29 +1,29 @@
require 'shogun'
require 'modshogun'
require 'load'

traindat = load_dna('../data/fm_train_dna.dat')
testdat = load_dna('../data/fm_test_dna.dat')
parameter_list = {{traindat,testdat,4,0,false, false},{traindat,testdat,4,0,False,False}}

function kernel_comm_word_string_modular (fm_train_dna,fm_test_dna, order, gap, reverse, use_sign)
charfeat=StringCharFeatures(DNA)
charfeat=modshogun.StringCharFeatures(modshogun.DNA)
charfeat:set_features(fm_train_dna)
feats_train=StringWordFeatures(charfeat:get_alphabet())
feats_train=modshogun.StringWordFeatures(charfeat:get_alphabet())
feats_train:obtain_from_char(charfeat, order-1, order, gap, reverse)

preproc=SortWordString()
preproc=modshogun.SortWordString()
preproc:init(feats_train)
feats_train:add_preprocessor(preproc)
feats_train:apply_preprocessor()

charfeat=StringCharFeatures(DNA)
charfeat=modshogun.StringCharFeatures(modshogun.DNA)
charfeat:set_features(fm_test_dna)
feats_test=StringWordFeatures(charfeat:get_alphabet())
feats_test=modshogun.StringWordFeatures(charfeat:get_alphabet())
feats_test:obtain_from_char(charfeat, order-1, order, gap, reverse)
feats_test:add_preprocessor(preproc)
feats_test:apply_preprocessor()

kernel=CommWordStringKernel(feats_train, feats_train, use_sign)
kernel=modshogun.CommWordStringKernel(feats_train, feats_train, use_sign)

km_train=kernel:get_kernel_matrix()
kernel:init(feats_train, feats_test)
Expand Down
8 changes: 4 additions & 4 deletions examples/undocumented/lua_modular/kernel_gaussian_modular.lua
@@ -1,4 +1,4 @@
require 'shogun'
require 'modshogun'
require 'load'

traindat = load_numbers('../data/fm_train_real.dat')
Expand All @@ -8,10 +8,10 @@ parameter_list = {{traindat,testdat, 1.3},{traindat,testdat, 1.4}}

function kernel_gaussian_modular (fm_train_real,fm_test_real,width)

feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
feats_train=modshogun.RealFeatures(fm_train_real)
feats_test=modshogun.RealFeatures(fm_test_real)

kernel=GaussianKernel(feats_train, feats_train, width)
kernel=modshogun.GaussianKernel(feats_train, feats_train, width)

km_train=kernel:get_kernel_matrix()
kernel:init(feats_train, feats_test)
Expand Down
@@ -1,4 +1,4 @@
require 'shogun'
require 'modshogun'
require 'load'

traindat = load_numbers('../data/fm_train_byte.dat')
Expand All @@ -7,10 +7,10 @@ testdat = load_numbers('../data/fm_test_byte.dat')
parameter_list={{traindat,testdat},{traindat,testdat}}

function kernel_linear_byte_modular(fm_train_byte,fm_test_byte)
feats_train=ByteFeatures(fm_train_byte)
feats_test=ByteFeatures(fm_test_byte)
feats_train=modshogun.ByteFeatures(fm_train_byte)
feats_test=modshogun.ByteFeatures(fm_test_byte)

kernel=LinearKernel(feats_train, feats_train)
kernel=modshogun.LinearKernel(feats_train, feats_train)
km_train=kernel:get_kernel_matrix()

kernel:init(feats_train, feats_test)
Expand Down
10 changes: 5 additions & 5 deletions examples/undocumented/lua_modular/kernel_linear_word_modular.lua
@@ -1,4 +1,4 @@
require 'shogun'
require 'modshogun'
require 'load'

traindat = load_numbers('../data/fm_train_word.dat')
Expand All @@ -7,11 +7,11 @@ testdat = load_numbers('../data/fm_test_word.dat')
parameter_list={{traindat,testdat,1.2},{traindat,testdat,1.2}}

function kernel_linear_word_modular (fm_train_word,fm_test_word,scale)
feats_train=WordFeatures(fm_train_word)
feats_test=WordFeatures(fm_test_word)
feats_train=modshogun.WordFeatures(fm_train_word)
feats_test=modshogun.WordFeatures(fm_test_word)

kernel=LinearKernel(feats_train, feats_train)
kernel:set_normalizer(AvgDiagKernelNormalizer(scale))
kernel=modshogun.LinearKernel(feats_train, feats_train)
kernel:set_normalizer(modshogun.AvgDiagKernelNormalizer(scale))
kernel:init(feats_train, feats_train)

km_train=kernel:get_kernel_matrix()
Expand Down
@@ -1,4 +1,4 @@
require 'shogun'
require 'modshogun'
require 'load'

traindat = load_dna('../data/fm_train_dna.dat')
Expand All @@ -8,10 +8,10 @@ parameter_list = {{traindat,testdat,3},{traindat,testdat,20}}

function kernel_weighted_degree_string_modular (fm_train_dna,fm_test_dna,degree)

feats_train=StringCharFeatures(fm_train_dna, DNA)
feats_test=StringCharFeatures(fm_test_dna, DNA)
feats_train=modshogun.StringCharFeatures(fm_train_dna, modshogun.DNA)
feats_test=modshogun.StringCharFeatures(fm_test_dna, modshogun.DNA)

kernel=WeightedDegreeStringKernel(feats_train, feats_train, degree)
kernel=modshogun.WeightedDegreeStringKernel(feats_train, feats_train, degree)

weights = {}
for i = degree, 1, -1 do
Expand Down
@@ -1,14 +1,14 @@
require 'shogun'
require 'modshogun'
require 'load'

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

parameter_list = {{data}}

function preprocessor_isomap_modular(data)
features = RealFeatures(data)
features = modshogun.RealFeatures(data)

preprocessor = Isomap()
preprocessor = modshogun.Isomap()
preprocessor:set_target_dim(1)
preprocessor:apply_to_feature_matrix(features)

Expand Down
12 changes: 6 additions & 6 deletions examples/undocumented/lua_modular/regression_krr_modular.lua
@@ -1,4 +1,4 @@
require 'shogun'
require 'modshogun'
require 'load'

traindat = load_numbers('../data/fm_train_real.dat')
Expand All @@ -9,14 +9,14 @@ label_traindat = 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}}

function regression_krr_modular (fm_train,fm_test,label_train,width,tau)
feats_train=RealFeatures(fm_train)
feats_test=RealFeatures(fm_test)
feats_train=modshogun.RealFeatures(fm_train)
feats_test=modshogun.RealFeatures(fm_test)

kernel=GaussianKernel(feats_train, feats_train, width)
kernel=modshogun.GaussianKernel(feats_train, feats_train, width)

labels=Labels(label_train)
labels=modshogun.Labels(label_train)

krr=KRR(tau, kernel, labels)
krr=modshogun.KRR(tau, kernel, labels)
krr:train(feats_train)

kernel:init(feats_train, feats_test)
Expand Down

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