def test_construction(self):
d = self.iris
petal_length = d.columns.petal_length
disc = distribution.Continuous(d, "petal length")
disc7 = distribution.Continuous(self.freqs)
self.assertIsInstance(disc, np.ndarray)
self.assertIsNone(disc7.variable)
self.assertEqual(disc7.unknowns, 0)
self.assertEqual(disc, disc7)
disc7 = distribution.Continuous(self.freqs, petal_length)
self.assertIsInstance(disc, np.ndarray)
self.assertIs(disc7.variable, petal_length)
self.assertEqual(disc7.unknowns, 0)
self.assertEqual(disc, disc7)
disc1 = distribution.Continuous(10, petal_length)
self.assertIsInstance(disc1, np.ndarray)
self.assertIs(disc7.variable, petal_length)
self.assertEqual(disc7.unknowns, 0)
assert_dist_equal(disc1, np.zeros((2, 10)))
dd = [list(range(5)), [1, 1, 2, 5, 1]]
disc2 = distribution.Continuous(dd)
self.assertIsInstance(disc2, np.ndarray)
self.assertIsNone(disc2.variable)
self.assertEqual(disc2.unknowns, 0)
assert_dist_equal(disc2, dd)
def test_normalize(self):
d = self.iris
petal_length = d.columns.petal_length
disc = distribution.Continuous(d, "petal length")
assert_dist_equal(disc, self.freqs)
disc.normalize()
self.freqs[1, :] /= 150
assert_dist_equal(disc, self.freqs)
disc1 = distribution.Continuous(10, petal_length)
disc1.normalize()
f = np.zeros((2, 10))
f[1, :] = 0.1
assert_dist_equal(disc1, f)
def test_hash(self):
d = self.iris
petal_length = d.columns.petal_length
disc = distribution.Continuous(d, "petal length")
disc2 = distribution.Continuous(d, petal_length)
self.assertEqual(hash(disc), hash(disc2))
disc2[0, 0] += 1
self.assertNotEqual(hash(disc), hash(disc2))
disc2[0, 0] -= 1
self.assertEqual(hash(disc), hash(disc2))
disc2.unknowns += 1
self.assertNotEqual(hash(disc), hash(disc2))
def test_normalize(self):
d = data.Table("iris")
petal_length = d.columns.petal_length
disc = distribution.Continuous(d, "petal length")
np.testing.assert_almost_equal(disc, self.freqs)
disc.normalize()
self.freqs[1, :] /= 150
np.testing.assert_almost_equal(disc, self.freqs)
disc1 = distribution.Continuous(10, petal_length)
disc1.normalize()
f = np.zeros((2, 10))
f[1, :] = 0.1
np.testing.assert_almost_equal(disc1, f)
def test_standard_deviation(self):
# pylint: disable=bad-whitespace
x = np.array([[0, 5, 10],
[9, 0, 1]])
dist = distribution.Continuous(x)
self.assertEqual(dist.standard_deviation(), np.std(([0] * 9) + [10]))
def test_variance(self):
# pylint: disable=bad-whitespace
x = np.array([[0, 5, 10],
[9, 0, 1]])
dist = distribution.Continuous(x)
self.assertEqual(dist.variance(), np.var(([0] * 9) + [10]))
def test_random(self):
d = self.iris
disc = distribution.Continuous(d, "petal length")
ans = set()
for i in range(1000):
v = disc.sample()
self.assertIn(v, self.freqs)
ans.add(v)
self.assertGreater(len(ans), 10)
def test_from_table(self):
d = self.iris
petal_length = d.columns.petal_length
for attr in ["petal length", d.domain[2], 2]:
disc = distribution.Continuous(d, attr)
self.assertIsInstance(disc, np.ndarray)
self.assertIs(disc.variable, petal_length)
self.assertEqual(disc.unknowns, 0)
assert_dist_almost_equal(disc, self.freqs)
def test_from_table(self):
d = data.Table("iris")
petal_length = d.columns.petal_length
disc = distribution.Continuous(d, "petal length")
self.assertIsInstance(disc, np.ndarray)
self.assertIs(disc.variable, petal_length)
self.assertEqual(disc.unknowns, 0)
np.testing.assert_almost_equal(disc, self.freqs)
disc2 = distribution.Continuous(d, d.domain[2])
self.assertIsInstance(disc2, np.ndarray)
self.assertIs(disc2.variable, petal_length)
self.assertEqual(disc, disc2)
disc3 = distribution.Continuous(d, 2)
self.assertIsInstance(disc3, np.ndarray)
self.assertIs(disc3.variable, petal_length)
self.assertEqual(disc, disc3)
def test_random(self):
d = data.Table("iris")
disc = distribution.Continuous(d, "petal length")
ans = set()
for i in range(1000):
v = disc.random()
self.assertTrue(v in self.freqs)
ans.add(v)
self.assertGreater(len(ans), 10)
def test_deepcopy(self):
d1 = distribution.Continuous(self.iris, 0)
dc = copy.deepcopy(d1)
# This always worked because `other` wasn't required to have `unknowns`
self.assertEqual(d1, dc)
# This failed before implementing `__deepcopy__`
self.assertEqual(dc, d1)
self.assertEqual(hash(d1), hash(dc))
# Test that `dc` has the required attributes
self.assertEqual(dc.variable, d1.variable)
self.assertEqual(dc.unknowns, d1.unknowns)
def test_modus(self):
disc = distribution.Continuous([list(range(5)), [1, 1, 2, 5, 1]])
self.assertEqual(disc.modus(), 3)
def test_modus(self):
d = data.Table("iris")
petal_length = d.columns.petal_length
disc = distribution.Continuous([list(range(5)), [1, 1, 2, 5, 1]])
self.assertEqual(disc.modus(), 3)
