Code Duplication - wmkouw/libTLDA - Measure and Improve Code Quality continuously with Scrutinizer

libTLDA

Code Duplication Length = 10-10 lines in 2 locations

libtlda/tests/test_iw.py 1 location


    assert clf.is_trained


def test_predict():
    """Test for making predictions."""
    X = rnd.randn(10, 2)
    y = np.hstack((-np.ones((5,)), np.ones((5,))))
    Z = rnd.randn(10, 2) + 1
    clf = ImportanceWeightedClassifier()
    clf.fit(X, y, Z)
    u_pred = clf.predict(Z)
    labels = np.unique(y)
    assert len(np.setdiff1d(np.unique(u_pred), labels)) == 0


libtlda/tests/test_suba.py 1 location


    assert clf.is_trained


def test_predict():
    """Test for making predictions."""
    X = rnd.randn(10, 2)
    y = np.hstack((-np.ones((5,)), np.ones((5,))))
    Z = rnd.randn(10, 2) + 1
    clf = SubspaceAlignedClassifier()
    clf.fit(X, y, Z)
    u_pred = clf.predict(Z)
    labels = np.unique(y)
    assert len(np.setdiff1d(np.unique(u_pred), labels)) == 0


		@@ 73-82 (lines=10) @@
70		assert clf.is_trained
71
72
73		def test_predict():
74		"""Test for making predictions."""
75		X = rnd.randn(10, 2)
76		y = np.hstack((-np.ones((5,)), np.ones((5,))))
77		Z = rnd.randn(10, 2) + 1
78		clf = ImportanceWeightedClassifier()
79		clf.fit(X, y, Z)
80		u_pred = clf.predict(Z)
81		labels = np.unique(y)
82		assert len(np.setdiff1d(np.unique(u_pred), labels)) == 0
83

		@@ 28-37 (lines=10) @@
25		assert clf.is_trained
26
27
28		def test_predict():
29		"""Test for making predictions."""
30		X = rnd.randn(10, 2)
31		y = np.hstack((-np.ones((5,)), np.ones((5,))))
32		Z = rnd.randn(10, 2) + 1
33		clf = SubspaceAlignedClassifier()
34		clf.fit(X, y, Z)
35		u_pred = clf.predict(Z)
36		labels = np.unique(y)
37		assert len(np.setdiff1d(np.unique(u_pred), labels)) == 0
38

wmkouw / libTLDA

Code Duplication Length = 10-10 lines in 2 locations

libtlda/tests/test_iw.py 1 location

libtlda/tests/test_suba.py 1 location