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from numpy.testing import *
import numpy as N
set_local_path('../..')
from svm.dataset import LibSvmOneClassDataSet, LibSvmTestDataSet
from svm.kernel import *
from svm.oneclass import *
from svm.predict import *
restore_path()
class test_oneclass(NumpyTestCase):
def check_basics(self):
ModelType = LibSvmOneClassModel
kernel = LinearKernel()
ModelType(kernel)
ModelType(kernel, nu=1.0)
def _make_basic_datasets(self):
x = [N.array([0, 0]),
N.array([0, 1]),
N.array([1, 0]),
N.array([1, 1])]
traindata = LibSvmOneClassDataSet(x)
testdata = LibSvmTestDataSet(x)
return traindata, testdata
def check_train(self):
traindata, testdata = self._make_basic_datasets()
model = LibSvmOneClassModel(LinearKernel())
results = model.fit(traindata)
p = results.predict(testdata)
assert_array_equal(p, [False, False, False, True])
v = results.predict_values(testdata)
assert_array_equal(v, [-0.5, 0.0, 0.0, 0.5])
def check_more(self):
traindata, testdata = self._make_basic_datasets()
ModelType = LibSvmOneClassModel
nu = 0.5
kernels = [
LinearKernel(),
PolynomialKernel(3, traindata.gamma, 0.0),
RBFKernel(traindata.gamma)
]
expected_preds = [
[False, False, False, True],
[False, False, False, True],
[True, False, False, False]
]
for kernel, expected_pred in zip(kernels, expected_preds):
model = ModelType(kernel, nu)
results = model.fit(traindata)
pred = results.predict(testdata)
self.assertEqual(results.predict(testdata), expected_pred)
values = results.predict_values(testdata)
for p, v in zip(pred, values):
self.assertEqual(v > 0, p)
def check_compact(self):
traindata, testdata = self._make_basic_datasets()
model = LibSvmOneClassModel(LinearKernel())
results = model.fit(traindata, LibSvmPythonPredictor)
refv = results.predict_values(testdata)
results.compact()
v = results.predict_values(testdata)
assert_array_equal(refv, v)
if __name__ == '__main__':
NumpyTest().run()
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