add minimal libsvm lua modular

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)

src/interfaces/lua_modular/shogun.lua

@@ -152,3 +152,4 @@ WordFeatures = modshogun.WordFeatures
 AvgDiagKernelNormalizer = modshogun.AvgDiagKernelNormalizer
 StringUlongFeatures = modshogun.StringUlongFeatures
 StringWordFeatures = modshogun.StringWordFeatures
+LibSVM = modshogun.LibSVM