Merge pull request #217 from sploving/master

add some more lua examples

examples/undocumented/lua_modular/evaluation_contingencytableevaluation_modular.lua

@@ -0,0 +1,53 @@
+require 'shogun'
+require 'load'
+
+ground_truth = load_labels('../data/label_train_twoclass.dat')
+math.randomseed(17)
+
+predicted = {}
+for i = 1, #ground_truth do
+	table.insert(predicted, math.random())
+end
+parameter_list = {{ground_truth,predicted}}
+
+function evaluation_contingencytableevaluation_modular(ground_truth, predicted)
+
+	ground_truth_labels = Labels(ground_truth)
+	predicted_labels = Labels(predicted)
+
+	base_evaluator = ContingencyTableEvaluation()
+	base_evaluator:evaluate(predicted_labels,ground_truth_labels)
+
+	evaluator = AccuracyMeasure()	
+	accuracy = evaluator:evaluate(predicted_labels,ground_truth_labels)
+
+	evaluator = ErrorRateMeasure()
+	errorrate = evaluator:evaluate(predicted_labels,ground_truth_labels)
+
+	evaluator = BALMeasure()
+	bal = evaluator:evaluate(predicted_labels,ground_truth_labels)
+
+	evaluator = WRACCMeasure()
+	wracc = evaluator:evaluate(predicted_labels,ground_truth_labels)
+
+	evaluator = F1Measure()
+	f1 = evaluator:evaluate(predicted_labels,ground_truth_labels)
+
+	evaluator = CrossCorrelationMeasure()
+	crosscorrelation = evaluator:evaluate(predicted_labels,ground_truth_labels)
+
+	evaluator = RecallMeasure()
+	recall = evaluator:evaluate(predicted_labels,ground_truth_labels)
+
+	evaluator = PrecisionMeasure()
+	precision = evaluator:evaluate(predicted_labels,ground_truth_labels)
+
+	evaluator = SpecificityMeasure()
+	specificity = evaluator:evaluate(predicted_labels,ground_truth_labels)
+
+	return accuracy, errorrate, bal, wracc, f1, crosscorrelation, recall, precision, specificity
+end
+
+print 'ContingencyTableEvaluation'
+evaluation_contingencytableevaluation_modular(unpack(parameter_list[1]))
+

examples/undocumented/lua_modular/features_simple_real_modular.lua

@@ -0,0 +1,17 @@
+require 'shogun'
+
+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:set_feature_vector({1,4,0,0,0,9}, 0)
+
+	a_out = a:get_feature_matrix()
+
+	return a_out
+end
+
+print 'simple_real'
+features_simple_real_modular(unpack(parameter_list[1]))

examples/undocumented/lua_modular/preprocessor_classicisomap_modular.lua

@@ -0,0 +1,20 @@
+require 'shogun'
+require 'load'
+
+data = load_numbers('../data/fm_train_real.dat')
+
+parameter_list = {{data}}
+
+function preprocessor_classicisomap_modular(data)
+	features = RealFeatures(data)
+
+	preprocessor = ClassicIsomap()
+	preprocessor:set_target_dim(1)
+	preprocessor:apply_to_feature_matrix(features)
+
+	return features
+end
+
+print 'ClassicIsomap'
+preprocessor_classicisomap_modular(unpack(parameter_list[1]))
+

examples/undocumented/lua_modular/regression_krr_modular.lua

@@ -0,0 +1,28 @@
+require 'shogun'
+require 'load'
+
+traindat = load_numbers('../data/fm_train_real.dat')
+testdat = load_numbers('../data/fm_test_real.dat')
+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)
+
+	kernel=GaussianKernel(feats_train, feats_train, width)
+
+	labels=Labels(label_train)
+
+	krr=KRR(tau, kernel, labels)
+	krr:train(feats_train)
+
+	kernel:init(feats_train, feats_test)
+	out = krr:apply():get_labels()
+	return out,kernel,krr
+end
+
+print 'KRR'
+regression_krr_modular(unpack(parameter_list[1]))

src/interfaces/lua_modular/shogun.lua

@@ -129,3 +129,14 @@ SortWordString = modshogun.SortWordString
 SortWordString = modshogun.SortWordString
 SparsePreprocessor = modshogun.SparsePreprocessor
 StringPreprocessor = modshogun.StringPreprocessor
+ContingencyTableEvaluation = modshogun.ContingencyTableEvaluation
+AccuracyMeasure = modshogun.AccuracyMeasure
+ErrorRateMeasure = modshogun.ErrorRateMeasure
+BALMeasure = modshogun.BALMeasure
+WRACCMeasure = modshogun.WRACCMeasure
+F1Measure = modshogun.F1Measure
+CrossCorrelationMeasure = modshogun.CrossCorrelationMeasure
+RecallMeasure = modshogun.RecallMeasure
+PrecisionMeasure = modshogun.PrecisionMeasure
+SpecificityMeasure = modshogun.SpecificityMeasure
+ClassicIsomap = modshogun.ClassicIsomap