def test_iris(self):
# Generate full set of constraints for comparison with reference implementation
mask = (self.iris_labels[None] == self.iris_labels[:, None])
a, b = np.nonzero(np.triu(mask, k=1))
c, d = np.nonzero(np.triu(~mask, k=1))
# Full metric
mmc = MMC(convergence_threshold=0.01)
mmc.fit(self.iris_points, [a, b, c, d])
expected = [[+0.00046504, +0.00083371, -0.00111959, -0.00165265],
[+0.00083371, +0.00149466, -0.00200719, -0.00296284],
[-0.00111959, -0.00200719, +0.00269546, +0.00397881],
[-0.00165265, -0.00296284, +0.00397881, +0.00587320]]
assert_array_almost_equal(expected, mmc.metric(), decimal=6)
# Diagonal metric
mmc = MMC(diagonal=True)
mmc.fit(self.iris_points, [a, b, c, d])
expected = [0, 0, 1.21045968, 1.22552608]
assert_array_almost_equal(np.diag(expected), mmc.metric(), decimal=6)
# Supervised Full
mmc = MMC_Supervised()
mmc.fit(self.iris_points, self.iris_labels)
csep = class_separation(mmc.transform(), self.iris_labels)
self.assertLess(csep, 0.15)
# Supervised Diagonal
mmc = MMC_Supervised(diagonal=True)
mmc.fit(self.iris_points, self.iris_labels)
csep = class_separation(mmc.transform(), self.iris_labels)
self.assertLess(csep, 0.2)
def test_iris(self):
# Generate full set of constraints for comparison with reference implementation
n = self.iris_points.shape[0]
mask = (self.iris_labels[None] == self.iris_labels[:, None])
a, b = np.nonzero(np.triu(mask, k=1))
c, d = np.nonzero(np.triu(~mask, k=1))
# Full metric
mmc = MMC(convergence_threshold=0.01)
mmc.fit(self.iris_points, [a, b, c, d])
expected = [[0.000514, 0.000868, -0.001195, -0.001703],
[0.000868, 0.001468, -0.002021, -0.002879],
[-0.001195, -0.002021, 0.002782, 0.003964],
[-0.001703, -0.002879, 0.003964, 0.005648]]
assert_array_almost_equal(expected, mmc.metric(), decimal=6)
# Diagonal metric
mmc = MMC(diagonal=True)
mmc.fit(self.iris_points, [a, b, c, d])
expected = [0, 0, 1.210220, 1.228596]
assert_array_almost_equal(np.diag(expected), mmc.metric(), decimal=6)
# Supervised Full
mmc = MMC_Supervised()
mmc.fit(self.iris_points, self.iris_labels)
csep = class_separation(mmc.transform(), self.iris_labels)
self.assertLess(csep, 0.15)
# Supervised Diagonal
mmc = MMC_Supervised(diagonal=True)
mmc.fit(self.iris_points, self.iris_labels)
csep = class_separation(mmc.transform(), self.iris_labels)
self.assertLess(csep, 0.2)
def test_iris(self):
# Generate full set of constraints for comparison with reference implementation
mask = (self.iris_labels[None] == self.iris_labels[:, None])
a, b = np.nonzero(np.triu(mask, k=1))
c, d = np.nonzero(np.triu(~mask, k=1))
# Full metric
mmc = MMC(convergence_threshold=0.01)
mmc.fit(self.iris_points, [a, b, c, d])
expected = [[+0.00046504, +0.00083371, -0.00111959, -0.00165265],
[+0.00083371, +0.00149466, -0.00200719, -0.00296284],
[-0.00111959, -0.00200719, +0.00269546, +0.00397881],
[-0.00165265, -0.00296284, +0.00397881, +0.00587320]]
assert_array_almost_equal(expected, mmc.metric(), decimal=6)
# Diagonal metric
mmc = MMC(diagonal=True)
mmc.fit(self.iris_points, [a, b, c, d])
expected = [0, 0, 1.21045968, 1.22552608]
assert_array_almost_equal(np.diag(expected), mmc.metric(), decimal=6)
# Supervised Full
mmc = MMC_Supervised()
mmc.fit(self.iris_points, self.iris_labels)
csep = class_separation(mmc.transform(), self.iris_labels)
self.assertLess(csep, 0.15)
# Supervised Diagonal
mmc = MMC_Supervised(diagonal=True)
mmc.fit(self.iris_points, self.iris_labels)
csep = class_separation(mmc.transform(), self.iris_labels)
self.assertLess(csep, 0.2)