Package org.apache.commons.math3.optim.nonlinear.vector

Examples of org.apache.commons.math3.optim.nonlinear.vector.Weight


        PointVectorValuePair optimum =
            optimizer.optimize(new MaxEval(100),
                               circle.getModelFunction(),
                               circle.getModelFunctionJacobian(),
                               new Target(target),
                               new Weight(weights),
                               new InitialGuess(new double[] { 0, 0 }));
        Assert.assertEquals(-0.1517383071957963, optimum.getPointRef()[0], 1e-6);
        Assert.assertEquals(0.2074999736353867,  optimum.getPointRef()[1], 1e-6);
        Assert.assertEquals(0.04268731682389561, optimizer.getRMS(),       1e-8);
    }
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        final PointVectorValuePair optimum
            = optimizer.optimize(new MaxEval(100),
                                 problem.getModelFunction(),
                                 problem.getModelFunctionJacobian(),
                                 new Target(data[1]),
                                 new Weight(w),
                                 new InitialGuess(initial));

        final double[] actual = optimum.getPoint();
        for (int i = 0; i < actual.length; i++) {
            double expected = dataset.getParameter(i);
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    @Test(expected=MathUnsupportedOperationException.class)
    public void testConstraintsUnsupported() {
        createOptimizer().optimize(new MaxEval(100),
                                   new Target(new double[] { 2 }),
                                   new Weight(new double[] { 1 }),
                                   new InitialGuess(new double[] { 1, 2 }),
                                   new SimpleBounds(new double[] { -10, 0 },
                                                    new double[] { 20, 30 }));
    }
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        optimizer.optimize(new MaxEval(100),
                           circle.getModelFunction(),
                           circle.getModelFunctionJacobian(),
                           new Target(new double[] { 0, 0, 0, 0, 0 }),
                           new Weight(new double[] { 1, 1, 1, 1, 1 }),
                           new InitialGuess(new double[] { 98.680, 47.345 }));
    }
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        final PointVectorValuePair optimum
            = optimizer.optimize(new MaxEval(maxEval),
                                 model.getModelFunction(),
                                 model.getModelFunctionJacobian(),
                                 new Target(target),
                                 new Weight(weights),
                                 new InitialGuess(initialGuess));
        // Extract the coefficients.
        return optimum.getPointRef();
    }
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            PointVectorValuePair optimum
                = optimizer.optimize(new MaxEval(400 * (function.getN() + 1)),
                                     function.getModelFunction(),
                                     function.getModelFunctionJacobian(),
                                     new Target(function.getTarget()),
                                     new Weight(function.getWeight()),
                                     new InitialGuess(function.getStartPoint()));
            Assert.assertFalse(exceptionExpected);
            function.checkTheoreticalMinCost(optimizer.getRMS());
            function.checkTheoreticalMinParams(optimum);
        } catch (TooManyEvaluationsException e) {
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        PointVectorValuePair optimum
            = optimizer.optimize(new MaxEval(100),
                                 problem.getModelFunction(),
                                 problem.getModelFunctionJacobian(),
                                 problem.getTarget(),
                                 new Weight(new double[] { 1, 1, 1 }),
                                 new InitialGuess(new double[] { 0, 0, 0 }));
        Assert.assertTrue(FastMath.sqrt(optimizer.getTargetSize()) * optimizer.getRMS() > 0.6);

        optimizer.computeCovariances(optimum.getPoint(), 1.5e-14);
    }
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                                                  Precision.SAFE_MIN);
            optimizer.optimize(new MaxEval(maxCostEval),
                               problem,
                               problemJacobian,
                               new Target(new double[] { 0, 0, 0, 0, 0 }),
                               new Weight(new double[] { 1, 1, 1, 1, 1 }),
                               new InitialGuess(new double[] { 98.680, 47.345 }));
            Assert.assertTrue(!shouldFail);
        } catch (DimensionMismatchException ee) {
            Assert.assertTrue(shouldFail);
        } catch (TooManyEvaluationsException ee) {
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        final PointVectorValuePair optimum
            = optimizer.optimize(new MaxEval(100),
                                 problem.getModelFunction(),
                                 problem.getModelFunctionJacobian(),
                                 new Target(dataPoints[1]),
                                 new Weight(weights),
                                 new InitialGuess(new double[] { 10, 900, 80, 27, 225 }));

        final double[] solution = optimum.getPoint();
        final double[] expectedSolution = { 10.4, 958.3, 131.4, 33.9, 205.0 };
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            = new LevenbergMarquardtOptimizer();
        final PointVectorValuePair optimum = optimizer.optimize(new MaxEval(100),
                                                                circle.getModelFunction(),
                                                                circle.getModelFunctionJacobian(),
                                                                new Target(circle.target()),
                                                                new Weight(circle.weight()),
                                                                new InitialGuess(init));

        final double[] paramFound = optimum.getPoint();

        // Retrieve errors estimation.
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