def trainSVM(kernel, labels):
#need to add an id number as the first column of the list
svmKernel = column_stack((arange(1, len(kernel.tolist()) + 1), kernel))
prob = svm_problem(labels.tolist(), svmKernel.tolist(), isKernel=True)
param = svm_parameter('-t 4')
model = svm_train(prob, param)
return model
def test_generator(self):
with assert_warns(FutureWarning):
column_stack((np.arange(3) for _ in range(2)))
def test_column_stack_and_vstack(self):
a=array([4.,2.])
b=array([2.,8.])
numpy.testing.assert_array_equal(column_stack((a[:,newaxis],b[:newaxis])), array([[4.,2.],[2.,8.]]))
numpy.testing.assert_array_equal(vstack((a[:,newaxis],b[:,newaxis])), array([[4.],[2.],[2.],[8.]]))
def test_generator(self):
with assert_warns(FutureWarning):
column_stack((np.arange(3) for _ in range(2)))
def predictSVM(model, testKernel):
svmTestKernel = column_stack(([0] * len(testKernel), testKernel))
#svm_predict returns 3 things - we want the first one, hense the [0] at the end of the following line
predictedLabels = svm_predict([0] * len(svmTestKernel), svmTestKernel.tolist(), model)[0]
return predictedLabels
