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import numpy as np
from numpy.testing import assert_allclose
import pytest
from astroML.linear_model import NadarayaWatson
def test_NW_simple():
X = np.arange(11.)
y = X + 1
dy = 1
# by symmetry, NW regression should get these exactly correct
Xfit = np.array([4, 5, 6])[:, None]
y_true = np.ravel(Xfit + 1)
clf = NadarayaWatson(h=0.5).fit(X[:, None], y, dy)
y_fit = clf.predict(Xfit)
assert_allclose(y_fit, y_true)
def test_NW_simple_laplacian_kernel():
X = np.arange(11.)
y = X + 1
dy = 1
# by symmetry, NW regression should get these exactly correct
Xfit = np.array([4, 5, 6])[:, None]
y_true = np.ravel(Xfit + 1)
kwargs = {'gamma': 10.}
clf = NadarayaWatson(kernel='laplacian', **kwargs).fit(X[:, None], y, dy)
y_fit = clf.predict(Xfit)
assert_allclose(y_fit, y_true)
def test_X_invalid_shape_exception():
X = np.arange(11.)
y = X + 1
dy = 1
clf = NadarayaWatson(h=0.5).fit(X[:, None], y, dy)
# not valid Xfit.shape[1], should raise an exception
Xfit = np.array([[4, 5, 6], [1, 2, 3]])
with pytest.raises(Exception) as e:
clf.predict(Xfit)
assert str(e.value) == "dimensions of X do not match training dimension"
# not valid Xfit.shape[1], should raise an exception
Xfit = np.array([4, 5, 6])
with pytest.raises(Exception) as e:
clf.predict(Xfit)
assert str(e.value) == "X must be two-dimensional"
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