diff --git a/data b/data
index 112a9393131..7fecaa29f5a 160000
--- a/data
+++ b/data
@@ -1 +1 @@
-Subproject commit 112a939313184cf97758623753dd6bac53f9a9bc
+Subproject commit 7fecaa29f5a2eb34191b05613e7f7ce717e64d0b
diff --git a/examples/meta/src/binary/perceptron.sg b/examples/meta/src/binary/perceptron.sg
index 48800f7a5f5..a51914779b6 100644
--- a/examples/meta/src/binary/perceptron.sg
+++ b/examples/meta/src/binary/perceptron.sg
@@ -10,12 +10,9 @@ BinaryLabels labels_train(f_labels_train)
 BinaryLabels labels_test(f_labels_test)
 #![create_features]
 
-#![set_parameters]
-real learn_rate = 1.0
-int max_iter = 1000
-#![set_parameters]
-
 #![create_instance]
+real learn_rate=1.0
+int max_iter=1000
 Perceptron perceptron(features_train, labels_train)
 perceptron.set_learn_rate(learn_rate)
 perceptron.set_max_iter(max_iter)
@@ -23,13 +20,14 @@ perceptron.set_max_iter(max_iter)
 
 #![train_and_apply]
 perceptron.train()
-BinaryLabels labels_predict = perceptron.apply_binary(features_test)
+perceptron.set_features(features_test)
+Labels labels_predict = perceptron.apply()
 #![train_and_apply]
 
-#![extract_weights_bias]
-RealVector w = perceptron.get_w()
-real b = perceptron.get_bias()
-#![extract_weights_bias]
+#![extract_weights]
+RealVector weights = perceptron.get_w()
+real bias = perceptron.get_bias()
+#![extract_weights]
 
 #![evaluate_accuracy]
 AccuracyMeasure eval()
diff --git a/examples/meta/src/neural_nets/classification_perceptron.sg b/examples/meta/src/neural_nets/classification_perceptron.sg
deleted file mode 100644
index a51914779b6..00000000000
--- a/examples/meta/src/neural_nets/classification_perceptron.sg
+++ /dev/null
@@ -1,35 +0,0 @@
-CSVFile f_feats_train("../../data/classifier_binary_2d_linear_features_train.dat")
-CSVFile f_feats_test("../../data/classifier_binary_2d_linear_features_test.dat")
-CSVFile f_labels_train("../../data/classifier_binary_2d_linear_labels_train.dat")
-CSVFile f_labels_test("../../data/classifier_binary_2d_linear_labels_test.dat")
-
-#![create_features]
-RealFeatures features_train(f_feats_train)
-RealFeatures features_test(f_feats_test)
-BinaryLabels labels_train(f_labels_train)
-BinaryLabels labels_test(f_labels_test)
-#![create_features]
-
-#![create_instance]
-real learn_rate=1.0
-int max_iter=1000
-Perceptron perceptron(features_train, labels_train)
-perceptron.set_learn_rate(learn_rate)
-perceptron.set_max_iter(max_iter)
-#![create_instance]
-
-#![train_and_apply]
-perceptron.train()
-perceptron.set_features(features_test)
-Labels labels_predict = perceptron.apply()
-#![train_and_apply]
-
-#![extract_weights]
-RealVector weights = perceptron.get_w()
-real bias = perceptron.get_bias()
-#![extract_weights]
-
-#![evaluate_accuracy]
-AccuracyMeasure eval()
-real accuracy = eval.evaluate(labels_predict, labels_test)
-#![evaluate_accuracy]
diff --git a/examples/undocumented/python/classifier_perceptron.py b/examples/undocumented/python/classifier_perceptron.py
deleted file mode 100644
index 7d6743f40a5..00000000000
--- a/examples/undocumented/python/classifier_perceptron.py
+++ /dev/null
@@ -1,42 +0,0 @@
-#!/usr/bin/env python
-
-from numpy import *
-
-parameter_list = [[100, 2, 5,1.,1000,1,1], [100, 2, 5,1.,1000,1,2]]
-
-def classifier_perceptron (n=100, dim=2, distance=5,learn_rate=1.,max_iter=1000,num_threads=1,seed=1):
-	from shogun import RealFeatures, BinaryLabels
-	from shogun import Perceptron
-
-	random.seed(seed)
-
-	# produce some (probably) linearly separable training data by hand
-	# Two Gaussians at a far enough distance
-	X=array(random.randn(dim,n))+distance
-	Y=array(random.randn(dim,n))-distance
-	X_test=array(random.randn(dim,n))+distance
-	Y_test=array(random.randn(dim,n))-distance
-	label_train_twoclass=hstack((ones(n), -ones(n)))
-
-	#plot(X[0,:], X[1,:], 'x', Y[0,:], Y[1,:], 'o')
-	fm_train_real=hstack((X,Y))
-	fm_test_real=hstack((X_test,Y_test))
-
-	feats_train=RealFeatures(fm_train_real)
-	feats_test=RealFeatures(fm_test_real)
-
-	labels=BinaryLabels(label_train_twoclass)
-
-	perceptron=Perceptron(feats_train, labels)
-	perceptron.set_learn_rate(learn_rate)
-	perceptron.set_max_iter(max_iter)
-	# only guaranteed to converge for separable data
-	perceptron.train()
-
-	perceptron.set_features(feats_test)
-	out_labels = perceptron.apply().get_labels()
-	return perceptron, out_labels
-
-if __name__=='__main__':
-	print('Perceptron')
-	classifier_perceptron(*parameter_list[0])