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src/b/i/biopython-1.63/Tests/test_LogisticRegression.py biopython(Download)
def test_classify(self): model = LogisticRegression.train(xs, ys) result = LogisticRegression.classify(model, [6, -173.143442352]) self.assertEqual(result, 1) result = LogisticRegression.classify(model, [309, -271.005880394])
predictions = [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0] for i in range(len(predictions)): prediction = LogisticRegression.classify(model, xs[i]) self.assertEqual(prediction, predictions[i]) if prediction==ys[i]:
for i in range(len(predictions)): model = LogisticRegression.train(xs[:i]+xs[i+1:], ys[:i]+ys[i+1:]) prediction = LogisticRegression.classify(model, xs[i]) self.assertEqual(prediction, predictions[i]) if prediction==ys[i]:
src/b/i/biopython-HEAD/Tests/test_LogisticRegression.py biopython(Download)
def test_classify(self): model = LogisticRegression.train(xs, ys) result = LogisticRegression.classify(model, [6, -173.143442352]) self.assertEqual(result, 1) result = LogisticRegression.classify(model, [309, -271.005880394])
predictions = [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0] for i in range(len(predictions)): prediction = LogisticRegression.classify(model, xs[i]) self.assertEqual(prediction, predictions[i]) if prediction==ys[i]:
for i in range(len(predictions)): model = LogisticRegression.train(xs[:i]+xs[i+1:], ys[:i]+ys[i+1:]) prediction = LogisticRegression.classify(model, xs[i]) self.assertEqual(prediction, predictions[i]) if prediction==ys[i]: