def test_values(self):
# information gain is np.log2(len(histogram)) - entropy(histogram)
# histogram with zeros
self.assertTrue(_information_gain([0, 0, 0]) == np.log2(3))
# uniform histogram
self.assertTrue(_information_gain([1, 1, 1]) == 0)
# use the examples of the TestErrorHistogramHelperFunction test above
# A)
hist = [0, 4]
self.assertTrue(_information_gain(hist) == np.log2(2))
# B)
hist = [0, 0, 4, 0]
self.assertTrue(_information_gain(hist) == np.log2(4))
# C)
hist = [4, 0, 0, 0]
self.assertTrue(_information_gain(hist) == np.log2(4))
# D)
hist = [0, 0, 3, 1]
self.assertTrue(np.allclose(_information_gain(hist), 1.18872187554))
# E)
hist = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 8, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
]
self.assertTrue(_information_gain(hist) == 4.4)
def test_values(self):
# information gain is np.log2(len(histogram)) - entropy(histogram)
# histogram with zeros
self.assertTrue(_information_gain([0, 0, 0]) == np.log2(3))
# uniform histogram
self.assertTrue(_information_gain([1, 1, 1]) == 0)
# use the examples of the TestErrorHistogramHelperFunction test above
# A)
hist = [0, 4]
self.assertTrue(_information_gain(hist) == np.log2(2))
# B)
hist = [0, 0, 4, 0]
self.assertTrue(_information_gain(hist) == np.log2(4))
# C)
hist = [4, 0, 0, 0]
self.assertTrue(_information_gain(hist) == np.log2(4))
# D)
hist = [0, 0, 3, 1]
self.assertTrue(np.allclose(_information_gain(hist), 1.18872187554))
# E)
hist = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
8, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
self.assertTrue(_information_gain(hist) == 4.4)
def test_types(self):
bins = _histogram_bins(4)
hist = _error_histogram(DETECTIONS, ANNOTATIONS, bins)
ig = _information_gain(hist)
self.assertIsInstance(ig, float)
def test_types(self):
bins = _histogram_bins(4)
hist = _error_histogram(DETECTIONS, ANNOTATIONS, bins)
ig = _information_gain(hist)
self.assertIsInstance(ig, float)
