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src/b/i/biopython-1.63/Tests/test_LogisticRegression.py   biopython(Download)
    def test_calculate_model(self):
        model = LogisticRegression.train(xs, ys)
        beta = model.beta
        self.assertAlmostEqual(beta[0],  8.9830, places=4)
        self.assertAlmostEqual(beta[1], -0.0360, places=4)
        self.assertAlmostEqual(beta[2],  0.0218, places=4)
 
    def test_classify(self):
        model = LogisticRegression.train(xs, ys)
    def test_calculate_probability(self):
        model = LogisticRegression.train(xs, ys)
        q, p = LogisticRegression.calculate(model, [6, -173.143442352])
        self.assertAlmostEqual(p, 0.993242, places=6)
        self.assertAlmostEqual(q, 0.006758, places=6)
    def test_model_accuracy(self):
        correct = 0
        model = LogisticRegression.train(xs, ys)
        predictions = [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]
        for i in range(len(predictions)):
    def test_leave_one_out(self):
        correct = 0
        predictions = [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0]
        for i in range(len(predictions)):
            model = LogisticRegression.train(xs[:i]+xs[i+1:], ys[:i]+ys[i+1:])

src/b/i/biopython-HEAD/Tests/test_LogisticRegression.py   biopython(Download)
    def test_calculate_model(self):
        model = LogisticRegression.train(xs, ys)
        beta = model.beta
        self.assertAlmostEqual(beta[0],  8.9830, places=4)
        self.assertAlmostEqual(beta[1], -0.0360, places=4)
        self.assertAlmostEqual(beta[2],  0.0218, places=4)
 
    def test_classify(self):
        model = LogisticRegression.train(xs, ys)
    def test_calculate_probability(self):
        model = LogisticRegression.train(xs, ys)
        q, p = LogisticRegression.calculate(model, [6, -173.143442352])
        self.assertAlmostEqual(p, 0.993242, places=6)
        self.assertAlmostEqual(q, 0.006758, places=6)
    def test_model_accuracy(self):
        correct = 0
        model = LogisticRegression.train(xs, ys)
        predictions = [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]
        for i in range(len(predictions)):
    def test_leave_one_out(self):
        correct = 0
        predictions = [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0]
        for i in range(len(predictions)):
            model = LogisticRegression.train(xs[:i]+xs[i+1:], ys[:i]+ys[i+1:])