def test_smooth_nn(self):
"""Test the univariate spline using local kernel smoothing"""
sm = smoothing.WeightedNearestNeighbour(3, 5, 0.5, False)
sm.initialize(self.data1, self.data2)
r = sm.predict(self.data1)
self.assertEqual(len(r), 8)
# This is slightly better than the NoCV variation
expected = [
5.2723735408560302, 7.5434782608695654, 8.6875, 9.5625, 10.75,
15.6, 7.6671586996151504, 6.2922201138519931
]
for a, b in zip(expected, r):
self.assertAlmostEqual(a, b)
sm = smoothing.WeightedNearestNeighbour(3, 5, 0.1, False)
sm.initialize(self.data1, self.data2)
r = sm.predict(self.data1)
self.assertEqual(len(r), 8)
# This is slightly better than the NoCV variation
expected = [
5.4637421665174575, 8.0223880597014929, 9.2750000000000004,
9.78125, 10.750000000000002, 15.6, 8.1320351120742185,
6.4280968201233994
]
for a, b in zip(expected, r):
self.assertAlmostEqual(a, b)
def test_smooth_nn(self):
"""Test the univariate spline using local kernel smoothing"""
# Test with regular parameters
sm = smoothing.WeightedNearestNeighbour(2, 5, 0.5, False)
sm.initialize(self.data1, self.data2)
r = sm.predict([5])
self.assertAlmostEqual(r[0], 4.85378590078)
r = sm.predict([15])
self.assertAlmostEqual(r[0], 14.472485768500951)
# Test with exponent
sm = smoothing.WeightedNearestNeighbour(2, 5, 0.5, False, exponent=2.0)
sm.initialize(self.data1, self.data2)
r = sm.predict([5])
self.assertAlmostEqual(r[0], 4.4223582231809182)
r = sm.predict([15])
self.assertAlmostEqual(r[0], 14.04993649085635)
sm = smoothing.WeightedNearestNeighbour(3, 5, 0.5, False)
sm.initialize(self.data1, self.data2)
r = sm.predict(self.data1)
self.assertEqual(len(r), 8)
# This is slightly better than the NoCV variation
expected = [
4.85378590078329, 7.3181818181818183, 8.6853448275862046,
10.054730258014073, 11.044451654769629, 14.497816294331514,
7.4375518352136076, 6.2364096080910238
]
for a, b in zip(expected, r):
self.assertAlmostEqual(a, b)
# Try with smaller mindiff => more weight on close neighbors
sm = smoothing.WeightedNearestNeighbour(3, 5, 0.1, False)
sm.initialize(self.data1, self.data2)
r = sm.predict(self.data1)
self.assertEqual(len(r), 8)
# This is slightly better than the NoCV variation
expected = [
4.3099526066350711, 7.0999999999999996, 8.8596153846153847,
10.610377054463424, 11.015838150289017, 14.1233812970026,
7.2064265084789056, 6.3871142393069835
]
for a, b in zip(expected, r):
self.assertAlmostEqual(a, b)