def _testTrainingOnClassificationDataset(self):
DS = ClassificationDataSet(2, class_labels=['Zero', 'One'])
DS.appendLinked([ 0, 0 ] , [0])
DS.appendLinked([ 0, 1 ] , [0])
DS.appendLinked([ 1, 0 ] , [0])
DS.appendLinked([ 1, 1 ] , [1])
network = buildNetwork(DS.indim, 5, 2, outclass=SoftmaxLayer)
trainer = BackpropTrainer( network, momentum=0.1, verbose=True, weightdecay=0.01)
nnf = NeuralNetworkFactory(network, trainer, seed=2, iterationsNum=20)
nnClassifier = nnf.buildClassifier(DS)
self.assertEqual(nnClassifier.getPrediction([0, 0]), 0)
self.assertEqual(nnClassifier.getPrediction([0, 1]), 0)
self.assertEqual(nnClassifier.getPrediction([1, 0]), 0)
self.assertEqual(nnClassifier.getPrediction([1, 1]), 1)
def testTrainingOnSepervisedDataset(self):
DS = SupervisedDataSet(2, 1)
DS.addSample([ 0, 0 ] , [0])
DS.addSample([ 0, 1 ] , [1])
DS.addSample([ 1, 0 ] , [1])
DS.addSample([ 1, 1 ] , [0])
network = N = buildNetwork(2, 4, 1)
trainer = BackpropTrainer(N, learningrate = 0.01, momentum = 0.99)
trainer.verbose = False
nnf = NeuralNetworkFactory(network, trainer, seed=2, iterationsNum=500)
nnClassifier = nnf.buildClassifier(DS)
self.assertAlmostEqual(nnClassifier.getPrediction([0, 0]), 0, delta=0.01)
self.assertAlmostEqual(nnClassifier.getPrediction([0, 1]), 1, delta=0.01)
self.assertAlmostEqual(nnClassifier.getPrediction([1, 0]), 1, delta=0.01)
self.assertAlmostEqual(nnClassifier.getPrediction([1, 1]), 0, delta=0.01)
