def test_poissonnb_prior():
"""Test whether class priors are properly set. """
Xp = X + np.array([2, 2])
clf = PoissonNB().fit(Xp, y)
assert_array_almost_equal(np.array([3, 3]) / 6.0, clf.class_prior_, 8)
clf.fit(X2, y2)
assert_array_almost_equal(clf.class_prior_.sum(), 1)
# Verify that np.log(clf.predict_proba(X)) gives the same results as
# clf.predict_log_proba(X)
y_pred_proba = clf.predict_proba(X2)
y_pred_log_proba = clf.predict_log_proba(X2)
assert_array_almost_equal(np.log(y_pred_proba), y_pred_log_proba, 8)
def test_poissonnb_prior():
"""Test whether class priors are properly set. """
Xp = X + np.array([2, 2])
clf = PoissonNB().fit(Xp, y)
assert_array_almost_equal(np.array([3, 3]) / 6.0,
clf.class_prior_, 8)
clf.fit(X2, y2)
assert_array_almost_equal(clf.class_prior_.sum(), 1)
# Verify that np.log(clf.predict_proba(X)) gives the same results as
# clf.predict_log_proba(X)
y_pred_proba = clf.predict_proba(X2)
y_pred_log_proba = clf.predict_log_proba(X2)
assert_array_almost_equal(np.log(y_pred_proba), y_pred_log_proba, 8)
def test_poissonnb():
clf = PoissonNB()
assert_raises(ValueError, clf.fit, -X2, y2)
y_pred = clf.fit(X2, y2).predict(X2)
assert_array_equal(y_pred, y2)
def test_poissonnb():
clf = PoissonNB()
assert_raises(ValueError, clf.fit, -X2, y2)
y_pred = clf.fit(X2, y2).predict(X2)
assert_array_equal(y_pred, y2)
