def test_radius_outlier_functional_response(self):
knnr = RadiusNeighborsRegressor(radius=0.001)
knnr.fit(self.X[3:6], self.X[3:6])
# No value given
with np.testing.assert_raises(ValueError):
knnr.predict(self.X[:10])
# Test response
knnr = RadiusNeighborsRegressor(radius=0.001,
outlier_response=self.X[0])
knnr.fit(self.X[:6], self.X[:6])
res = knnr.predict(self.X[:7])
np.testing.assert_array_almost_equal(self.X[0].data_matrix,
res[6].data_matrix)
def test_radius_functional_response(self):
knnr = RadiusNeighborsRegressor(metric=lp_distance,
weights='distance',
regressor=l2_mean)
knnr.fit(self.X, self.X)
res = knnr.predict(self.X)
np.testing.assert_array_almost_equal(res.data_matrix,
self.X.data_matrix)
def test_predict_regressor(self):
"""Test scalar regression, predics mode location"""
# Dummy test, with weight = distance, only the sample with distance 0
# will be returned, obtaining the exact location
knnr = KNeighborsRegressor(weights='distance')
rnnr = RadiusNeighborsRegressor(weights='distance', radius=.1)
knnr.fit(self.X, self.modes_location)
rnnr.fit(self.X, self.modes_location)
np.testing.assert_array_almost_equal(knnr.predict(self.X),
self.modes_location)
np.testing.assert_array_almost_equal(rnnr.predict(self.X),
self.modes_location)