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#3555 add meta_example for classifier_perceptron undocumented-sample #4140

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Feb 3, 2018
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#3555 add meta_example for classifier_perceptron undocumented-sample #4140

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#3555 add meta_example for classifier_perceptron undocumented-python-…
spothound Feb 1, 2018
7b57001
Integration testing data for perceptron classifier
spothound Feb 2, 2018
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2 changes: 1 addition & 1 deletion data
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Submodule data updated 1 files
+18 −0 testsuite/meta/neural_nets/classification_perceptron.dat
35 changes: 35 additions & 0 deletions examples/meta/src/neural_nets/classification_perceptron.sg
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CSVFile f_feats_train("../../data/classifier_binary_2d_linear_features_train.dat")
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ah, I had overseen this.
Can you pls send another PR where you move the perceptron to the "binary" folder, it does not really follow the NN structure in Shogun. Same for the data file.
Also could you remove the python example in the same PR?

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Ahh ok, I thought it had to be with NN examples, I'll move it and remove the python example...

Also... I'm seeing that one check didn't pass which is weird because I just send the data file... don't know :/

Well, I'lll do that and send another commit!

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another PR* xp

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]