def test_kerneldensitypredict():
x, y = data.categorical_2Dmatrix_bernoulli_data()
km = kernelmethods.KernelMethods()
km.fit(x, y)
predict = km.kerneldensitypredict([1, 2], 0.8)
assert predict == 0
def test_kerneldensityestimate():
x, y = data.continuous_data_complicated()
km = kernelmethods.KernelMethods()
km.fit(x, y)
kde = km.kerneldensityestimate([5], 1)
np.testing.assert_almost_equal(kde, 0)
def test_nadarayaaverage():
x, y = data.continuous_data_complicated()
km = kernelmethods.KernelMethods()
km.fit(x, y)
predict = km.nadarayaaverage(5.5, km.gaussiankernel, 0.01)
np.testing.assert_almost_equal(predict, 4.237, 2)
def test_locallinearregression():
x, y = data.continuous_data_complicated()
km = kernelmethods.KernelMethods()
km.fit(x, y)
predict = km.locallinearregression(5.5, km.gaussiankernel, 0.1)
np.testing.assert_almost_equal(predict, 5.092, 2)
def test_kernelmethods_fit():
x, y = data.continuous_data_complicated()
km = kernelmethods.KernelMethods()
km.fit(x, y)
assert km.learned