def task(self): listP = [] listData = [] for i in map(float, xrange(0, 101)): listP.append(i / 100.0) dblEntropy = dtree.compute_entropy(i, 100.0 - i) listData.append(dblEntropy) return { "chart": { "defaultSeriesType": "line" }, "title": { "text": "Entropy" }, "xAxis": { "title": { "text": "p" } }, "yAxis": { "title": { "text": "entropy" }, "min": 0, "max": 1.1 }, "series": [{ "name": "Entropy", "data": zip(listP, listData) }] }
def task(self): listP = [] listData = [] for i in map(float, xrange(0, 101)): listP.append(i / 100.0) dblEntropy = dtree.compute_entropy(i, 100.0 - i) listData.append(dblEntropy) return {"chart": {"defaultSeriesType": "line"}, "title": {"text": "Entropy"}, "xAxis": {"title": {"text": "p"}}, "yAxis": {"title": {"text": "entropy"}, "min": 0, "max": 1.1}, "series": [{"name": "Entropy", "data": zip(listP, listData)}]}
def test_compute_entropy(self): dblK = 1000000.0 * random.random() self.assertAlmostEqual(1.0, dtree.compute_entropy(dblK, dblK)) self.assertAlmostEqual(0.0, dtree.compute_entropy(0.0, dblK)) self.assertAlmostEqual(0.0, dtree.compute_entropy(dblK, 0.0))
def test_compute_entropy(self): dblK = 1000000.0*random.random() self.assertAlmostEqual(1.0, dtree.compute_entropy(dblK,dblK)) self.assertAlmostEqual(0.0, dtree.compute_entropy(0.0, dblK)) self.assertAlmostEqual(0.0, dtree.compute_entropy(dblK, 0.0))