Merge pull request #223 from sploving/master

add lua docs and further examples

doc/pages/Installation.mainpage

@@ -14,6 +14,7 @@ On debian, depending on the interface you want, install the package(s)
 \verbatim
 libshogun-dev         - for C++ developers building extensions using
                         libshogun
+shogun-lua-modular    - for the modular lua interface
 shogun-java-modular   - for the modular java interface
 shogun-octave         - for the static octave interface
 shogun-octave-modular - for the modular octave interface
@@ -69,6 +70,9 @@ CPLEX(tm) just make sure cplex can be found in the PATH. If it is not found
 shogun will resort to GLPK (if version at least 4.29 is found) for 1-norm MKL,
 p-norm MKL with p>1 will work nonetheless.
 
+\li To compile the Lua interface you need to have the Lua developer files 
+(debian package lua) installed.
+
 \li To compile the Java interface you need to have the Java developer files 
 (debian package openjdk-6-jdk) installed.
 
@@ -109,6 +113,8 @@ where \b interface is one of the following
 
 \li java_modular -- for java (see http://www.java.com).
 
+\li lua_modular -- for lua (see http://www.lua.org).
+
 \li matlab -- for matlab (see http://www.mathworks.com)
 
 (you don't need to compile libshogunui in case you compile a modular interface)

doc/pages/ModularTutorial.mainpage

@@ -156,8 +156,67 @@ DoubleMatrix trainlab = concatHorizontally(o.neg(), o);
 DoubleMatrix testlab = concatHorizontarily(o.neg(), o)
 \endverbatim
 
+In lua, this is is done as follows:
+\verbatim
+require 'shogun'
+require 'load'
+
+function concatenate(...)
+	local result = ...
+	for _,t in ipairs{select(2, ...)} do
+		for row,rowdata in ipairs(t) do
+			for col,coldata in ipairs(rowdata) do
+				table.insert(result[row], coldata)
+			end		
+		end
+	end
+	return result
+end
+
+function rand_matrix(rows, cols, dist)
+  local matrix = {}
+	for i = 1, rows do
+		matrix[i] = {}
+		for j = 1, cols do
+			matrix[i][j] = math.random() + dist
+		end	
+	end
+	return matrix
+end
+
+function ones(num)
+	r={}
+	for i=1,num do
+		r[i]=1
+	end
+	return r
+end
+
+
+num=1000
+dist=1
+width=2.1
+C=1
+
+traindata_real=concatenate(rand_matrix(2,num, -dist),rand_matrix(2,num,dist))
+testdata_real=concatenate(rand_matrix(2,num,-dist), rand_matrix(2,num, dist))
+trainlab={}
+for i = 1, num do
+	trainlab[i] = -1
+	trainlab[i + num] = 1
+end
+
+testlab={}
+for i = 1, num do
+	testlab[i] = -1
+	testlab[i + num] = 1
+end
+
+\endverbatim
+
 The rest of this tutorial below will now work the same (identical syntax) for python,
-octave (when using a trailing semicolon for each command, which is optional in python).
+octave (when using a trailing semicolon for each command, which is optional in python),
+lua(we use colon to get the method of an object).
 
 For java, we use DoubleMatrix to represent most of the types. If shogun C++ class need a 
 int Vector/Matrix, we convert the DoubleMatrix into int Vector/Matrix and when returned 
@@ -249,6 +308,10 @@ For java:
 
 \verbinclude classifier_libsvm_minimal_modular.java
 
+For lua:
+
+\verbinclude classifier_libsvm_minimal_modular.lua
+
 For python:
 \verbinclude classifier_libsvm_minimal_modular.py
 */

examples/undocumented/lua_modular/classifier_averaged_perceptron_modular.lua

@@ -22,8 +22,11 @@ function classifier_averaged_perceptron_modular (fm_train_real,fm_test_real,labe
 
 	perceptron:set_features(feats_test)
 	out_labels = perceptron:apply():get_labels()
+
 	return perceptron, out_labels
 end
 
-print 'AveragedPerceptron'
-classifier_averaged_perceptron_modular(unpack(parameter_list[1]))
+if debug.getinfo(3) == nill then
+	print 'AveragedPerceptron'
+	classifier_averaged_perceptron_modular(unpack(parameter_list[1]))
+end

examples/undocumented/lua_modular/classifier_libsvm_minimal_modular.lua

@@ -0,0 +1,78 @@
+require 'shogun'
+require 'load'
+
+function concatenate(...)
+	local result = ...
+	for _,t in ipairs{select(2, ...)} do
+		for row,rowdata in ipairs(t) do
+			for col,coldata in ipairs(rowdata) do
+				table.insert(result[row], coldata)
+			end		
+		end
+	end
+	return result
+end
+
+function rand_matrix(rows, cols, dist)
+  local matrix = {}
+	for i = 1, rows do
+		matrix[i] = {}
+		for j = 1, cols do
+			matrix[i][j] = math.random() + dist
+		end	
+	end
+	return matrix
+end
+
+function ones(num)
+	r={}
+	for i=1,num do
+		r[i]=1
+	end
+	return r
+end
+
+
+num=1000
+dist=1
+width=2.1
+C=1
+
+traindata_real=concatenate(rand_matrix(2,num, -dist),rand_matrix(2,num,dist))
+testdata_real=concatenate(rand_matrix(2,num,-dist), rand_matrix(2,num, dist))
+
+trainlab={}
+for i = 1, num do
+	trainlab[i] = -1
+	trainlab[i + num] = 1
+end
+
+testlab={}
+for i = 1, num do
+	testlab[i] = -1
+	testlab[i + num] = 1
+end
+
+feats_train=RealFeatures(traindata_real)
+feats_test=RealFeatures(testdata_real)
+kernel=GaussianKernel(feats_train, feats_train, width)
+
+labels=Labels(trainlab)
+svm=LibSVM(C, kernel, labels)
+svm:train()
+
+kernel:init(feats_train, feats_test)
+out=svm:apply():get_labels()
+
+err_num = 0
+for i = 1, num do 
+	if out[i] > 0 then
+		err_num = err_num+1
+	end
+	if out[i+num] < 0 then
+		err_num = err_num+1
+	end
+end
+
+testerr=err_num/(2*num)
+print(testerr)

examples/undocumented/lua_modular/distance_braycurtis_modular.lua

@@ -20,5 +20,7 @@ function distance_braycurtis_modular (fm_train_real,fm_test_real)
 	return distance,dm_train,dm_test
 end
 
-print 'BrayCurtisDistance'
-distance_braycurtis_modular(unpack(parameter_list[1]))
+if debug.getinfo(3) == nill then
+	print 'BrayCurtisDistance'
+	distance_braycurtis_modular(unpack(parameter_list[1]))
+end

examples/undocumented/lua_modular/evaluation_contingencytableevaluation_modular.lua

@@ -48,6 +48,8 @@ function evaluation_contingencytableevaluation_modular(ground_truth, predicted)
 	return accuracy, errorrate, bal, wracc, f1, crosscorrelation, recall, precision, specificity
 end
 
-print 'ContingencyTableEvaluation'
-evaluation_contingencytableevaluation_modular(unpack(parameter_list[1]))
+if debug.getinfo(3) == nill then
+	print 'ContingencyTableEvaluation'
+	evaluation_contingencytableevaluation_modular(unpack(parameter_list[1]))
+end
 

examples/undocumented/lua_modular/features_simple_real_modular.lua

@@ -13,5 +13,7 @@ function features_simple_real_modular(A)
 	return a_out
 end
 
-print 'simple_real'
-features_simple_real_modular(unpack(parameter_list[1]))
+if debug.getinfo(3) == nill then
+ 	print 'simple_real'
+	features_simple_real_modular(unpack(parameter_list[1]))
+end

examples/undocumented/lua_modular/kernel_comm_ulong_string_modular.lua

@@ -0,0 +1,39 @@
+require 'shogun'
+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:set_features(fm_train_dna)
+	feats_train=StringUlongFeatures(charfeat:get_alphabet())
+	feats_train:obtain_from_char(charfeat, order-1, order, gap, reverse)
+	preproc=SortUlongString()
+	preproc:init(feats_train)
+	feats_train:add_preprocessor(preproc)
+	feats_train:apply_preprocessor()
+
+
+	charfeat=StringCharFeatures(DNA)
+	charfeat:set_features(fm_test_dna)
+	feats_test=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)
+
+	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 debug.getinfo(3) == nill then
+	print 'CommUlongString'
+	kernel_comm_ulong_string_modular(unpack(parameter_list[1]))
+end

examples/undocumented/lua_modular/kernel_comm_word_string_modular.lua

@@ -0,0 +1,37 @@
+require 'shogun'
+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:set_features(fm_train_dna)
+	feats_train=StringWordFeatures(charfeat:get_alphabet())
+	feats_train:obtain_from_char(charfeat, order-1, order, gap, reverse)
+
+	preproc=SortWordString()
+	preproc:init(feats_train)
+	feats_train:add_preprocessor(preproc)
+	feats_train:apply_preprocessor()
+
+	charfeat=StringCharFeatures(DNA)
+	charfeat:set_features(fm_test_dna)
+	feats_test=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)
+
+	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 debug.getinfo(3) == nill then
+	print 'CommWordString'
+	kernel_comm_word_string_modular(unpack(parameter_list[1]))
+end

examples/undocumented/lua_modular/kernel_gaussian_modular.lua

@@ -20,5 +20,7 @@ function kernel_gaussian_modular (fm_train_real,fm_test_real,width)
 	return km_train,km_test,kernel
 end
 
-print 'Gaussian'
-kernel_gaussian_modular(unpack(parameter_list[1]))
+if debug.getinfo(3) == nill then
+	print 'Gaussian'
+	kernel_gaussian_modular(unpack(parameter_list[1]))
+end

examples/undocumented/lua_modular/kernel_linear_byte_modular.lua

@@ -0,0 +1,24 @@
+require 'shogun'
+require 'load'
+
+traindat = load_numbers('../data/fm_train_byte.dat')
+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)
+
+	kernel=LinearKernel(feats_train, feats_train)
+	km_train=kernel:get_kernel_matrix()
+
+	kernel:init(feats_train, feats_test)
+	km_test=kernel:get_kernel_matrix()
+	return kernel
+end
+
+if debug.getinfo(3) == nill then
+	print 'LinearByte'
+	kernel_linear_byte_modular(unpack(parameter_list[1]))
+end

examples/undocumented/lua_modular/kernel_linear_word_modular.lua

@@ -0,0 +1,26 @@
+require 'shogun'
+require 'load'
+
+traindat = load_numbers('../data/fm_train_word.dat')
+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)
+
+	kernel=LinearKernel(feats_train, feats_train)
+	kernel:set_normalizer(AvgDiagKernelNormalizer(scale))
+	kernel:init(feats_train, feats_train)
+
+	km_train=kernel:get_kernel_matrix()
+	kernel:init(feats_train, feats_test)
+	km_test=kernel:get_kernel_matrix()
+	return kernel
+end
+
+if debug.getinfo(3) == nill then
+	print 'LinearWord'
+	kernel_linear_word_modular(unpack(parameter_list[1]))
+end

examples/undocumented/lua_modular/kernel_weighted_degree_string_modular.lua

@@ -26,7 +26,6 @@ function kernel_weighted_degree_string_modular (fm_train_dna,fm_test_dna,degree)
 	return km_train, km_test, kernel
 end
 
-
 if debug.getinfo(3) == nill then
 	print 'WeightedDegreeString'
 	kernel_weighted_degree_string_modular(unpack(parameter_list[1]))

examples/undocumented/lua_modular/preprocessor_classicisomap_modular.lua

@@ -15,6 +15,8 @@ function preprocessor_classicisomap_modular(data)
 	return features
 end
 
-print 'ClassicIsomap'
-preprocessor_classicisomap_modular(unpack(parameter_list[1]))
+if debug.getinfo(3) == nill then
+	print 'ClassicIsomap'
+	preprocessor_classicisomap_modular(unpack(parameter_list[1]))
+end
 

examples/undocumented/lua_modular/serialization_complex_example.lua

@@ -116,5 +116,7 @@ function serialization_complex_example(num, dist, dim, C, width)
 	return svm,new_svm
 end
 
-print 'Serialization SVMLight'
-serialization_complex_example(unpack(parameter_list[1]))
+if debug.getinfo(3) == nill then
+	print 'Serialization SVMLight'
+	serialization_complex_example(unpack(parameter_list[1]))
+end