def testMulticlassTaskWithProbabilityEstimation(self):
# Coordinates plane for classification task
# 1111111
# X 01234567890123456 #Y
grid = [' 0 0 ', #0
' 0 0 2 2', #1
' ', #2
' 2 2', #3
' ', #4
' 1 1 ', #5
' ', #6
' 1 1 ', #7
]
dataset = ClassificationDataSet(2, nb_classes=3, class_labels= ['0', '1', '2'])
createFromGrid(grid, dataset)
svmf = SVMFactory(C=100, estimateProbability=True)
classifier = svmf.buildClassifier(dataset)
self.assertEqual(classifier.getPrediction([3, 0]), 0)
self.assertEqual(classifier.getPrediction([4, 1]), 0)
self.assertEqual(classifier.getPrediction([6, 5]), 1)
self.assertEqual(classifier.getPrediction([6, 6]), 1)
self.assertEqual(classifier.getPrediction([15, 2]), 2)
self.assertEqual(classifier.getPrediction([14, 2]), 2)
def testLinearSeparableNuSVCTypeEstimateProbability(self):
# Coordinates plane for classification task
# 1111111
# X 01234567890123456 #Y
grid = [' 1 1 ', #0
' 1 1 0 0', #1
' ', #2
' 0 0', #3
' ', #4
' 0 0 ', #5
' ', #6
' 0 0 ', #7
]
dataset = ClassificationDataSet(2, nb_classes=2, class_labels= ['0', '1'])
createFromGrid(grid, dataset)
svmf = SVMFactory(nu = 0.3, estimateProbability=True, svmType=SVMType.NU_SVC)
classifier = svmf.buildClassifier(dataset)
self.assertEqual(classifier.getPrediction([3, 0]), 1)
self.assertEqual(classifier.getPrediction([4, 1]), 1)
self.assertEqual(classifier.getPrediction([6, 5]), 0)
self.assertEqual(classifier.getPrediction([6, 6]), 0)
self.assertEqual(classifier.getPrediction([15, 2]), 0)
self.assertEqual(classifier.getPrediction([14, 2]), 0)