Exemple #1
0
    def test_modeling_4p(self):
        exponents = [(0, 1, 1), (0, 1, 2), (1, 4, 0), (1, 3, 0), (1, 4, 1),
                     (1, 3, 1), (1, 4, 2), (1, 3, 2), (1, 2, 0), (1, 2, 1),
                     (1, 2, 2), (2, 3, 0), (3, 4, 0), (2, 3, 1), (3, 4, 1),
                     (4, 5, 0), (2, 3, 2), (3, 4, 2), (1, 1, 0), (1, 1, 1),
                     (1, 1, 2), (5, 4, 0), (5, 4, 1), (4, 3, 0), (4, 3, 1),
                     (3, 2, 0), (3, 2, 1), (3, 2, 2), (5, 3, 0), (7, 4, 0),
                     (2, 1, 0), (2, 1, 1), (2, 1, 2), (9, 4, 0), (7, 3, 0),
                     (5, 2, 0), (5, 2, 1), (5, 2, 2), (8, 3, 0), (11, 4, 0),
                     (3, 1, 0), (3, 1, 1)]
        points = np.array(
            list(zip(*itertools.product([2, 4, 8, 10, 12], repeat=4))))
        for expo1, expo2, expo3, expo4 in zip(exponents, exponents[1:],
                                              exponents[2:], exponents[3:]):
            termX = CompoundTerm.create(*expo1)
            termY = CompoundTerm.create(*expo2)
            termZ = CompoundTerm.create(*expo3)
            termW = CompoundTerm.create(*expo4)
            term = MultiParameterTerm((0, termX), (1, termY), (2, termZ),
                                      (3, termW))
            term.coefficient = 10
            function = MultiParameterFunction(term)
            function.constant_coefficient = 20000

            values = function.evaluate(points)
            measurements = [
                Measurement(Coordinate(p), None, None, v)
                for p, v in zip(zip(*points), values)
            ]
            modeler = MultiParameterModeler()

            models = modeler.model([measurements])
            self.assertEqual(1, len(models))
            self.assertApproxFunction(function, models[0].hypothesis.function)
Exemple #2
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    def test_modeling_plus(self):
        exponents = [(0, 1, 1), (0, 1, 2), (1, 4, 0), (1, 3, 0), (1, 4, 1), (1, 3, 1), (1, 4, 2), (1, 3, 2),
                     (1, 2, 0), (1, 2, 1), (1, 2, 2), (2, 3, 0), (3, 4, 0), (2, 3, 1), (3, 4, 1), (4, 5, 0),
                     (2, 3, 2), (3, 4, 2), (1, 1, 0), (1, 1, 1), (1, 1, 2), (5, 4, 0), (5, 4, 1), (4, 3, 0),
                     (4, 3, 1), (3, 2, 0), (3, 2, 1), (3, 2, 2), (5, 3, 0), (7, 4, 0), (2, 1, 0), (2, 1, 1),
                     (2, 1, 2), (9, 4, 0), (7, 3, 0), (5, 2, 0), (5, 2, 1), (5, 2, 2), (8, 3, 0), (11, 4, 0),
                     (3, 1, 0), (3, 1, 1)]
        for expo1, expo2 in zip(exponents, exponents[1:]):
            termX = CompoundTerm.create(*expo1)
            termY = CompoundTerm.create(*expo2)
            term1 = MultiParameterTerm((0, termX))
            term1.coefficient = 10
            term2 = MultiParameterTerm((1, termY))
            term2.coefficient = 20
            function = MultiParameterFunction(term1, term2)
            function.constant_coefficient = 200
            points = [np.array([2, 4, 8, 16, 32, 2, 4, 8, 16, 32, 2, 4, 8, 16, 32, 2, 4, 8, 16, 32, 2, 4, 8, 16, 32]),
                      np.array([2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 8, 8, 8, 8, 8, 16, 16, 16, 16, 16, 32, 32, 32, 32, 32])]

            values = function.evaluate(np.array(points))
            measurements = [Measurement(Coordinate(p), None, None, v) for p, v in zip(zip(*points), values)]
            modeler = MultiParameterModeler()

            models = modeler.model([measurements])
            self.assertEqual(1, len(models))
            self.assertApproxFunction(function, models[0].hypothesis.function)