Package org.apache.commons.math3.optim

Examples of org.apache.commons.math3.optim.PointVectorValuePair$DataTransferObject

231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
        }

        final LevenbergMarquardtOptimizer optimizer
            = new LevenbergMarquardtOptimizer();

        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 };

        final double[][] covarMatrix = optimizer.computeCovariances(solution, 1e-14);
        final double[][] expectedCovarMatrix = {
            { 3.38, -3.69, 27.98, -2.34, -49.24 },
View Full Code Here
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
        // First guess for the center's coordinates and radius.
        final double[] init = { 90, 659, 115 };

        final LevenbergMarquardtOptimizer optimizer
            = 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.
        final double[] asymptoticStandardErrorFound = optimizer.computeSigma(paramFound, 1e-14);

        // Check that the parameters are found within the assumed error bars.
View Full Code Here
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
        RandomVectorGenerator generator
            = new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
        MultiStartMultivariateVectorOptimizer optimizer
            = new MultiStartMultivariateVectorOptimizer(underlyingOptimizer, 10, generator);

        PointVectorValuePair optimum
            = optimizer.optimize(new MaxEval(100),
                                 problem.getModelFunction(),
                                 problem.getModelFunctionJacobian(),
                                 problem.getTarget(),
                                 new Weight(new double[] { 1 }),
                                 new InitialGuess(new double[] { 0 }));
        Assert.assertEquals(1.5, optimum.getPoint()[0], 1e-10);
        Assert.assertEquals(3.0, optimum.getValue()[0], 1e-10);
        PointVectorValuePair[] optima = optimizer.getOptima();
        Assert.assertEquals(10, optima.length);
        for (int i = 0; i < optima.length; i++) {
            Assert.assertEquals(1.5, optima[i].getPoint()[0], 1e-10);
            Assert.assertEquals(3.0, optima[i].getValue()[0], 1e-10);
View Full Code Here
504
505
506
507
508
509
510
511
512
513
514
        LevenbergMarquardtOptimizer optimizer
            = new LevenbergMarquardtOptimizer(FastMath.sqrt(2.22044604926e-16),
                                              FastMath.sqrt(2.22044604926e-16),
                                              2.22044604926e-16);
        try {
            PointVectorValuePair optimum
                = optimizer.optimize(new MaxEval(400 * (function.getN() + 1)),
                                     function.getModelFunction(),
                                     function.getModelFunctionJacobian(),
                                     new Target(function.getTarget()),
                                     new Weight(function.getWeight()),
View Full Code Here
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
                {  2, 1,  3 },
                { -3, 0, -9 }
        }, new double[] { 1, 1, 1 });

        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        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);
    }
View Full Code Here
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
        }

        final LevenbergMarquardtOptimizer optimizer
            = new LevenbergMarquardtOptimizer();

        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 };

        final double[][] covarMatrix = optimizer.computeCovariances(solution, 1e-14);
        final double[][] expectedCovarMatrix = {
            { 3.38, -3.69, 27.98, -2.34, -49.24 },
View Full Code Here
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
        // First guess for the center's coordinates and radius.
        final double[] init = { 90, 659, 115 };

        final LevenbergMarquardtOptimizer optimizer
            = 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.
        final double[] asymptoticStandardErrorFound = optimizer.computeSigma(paramFound, 1e-14);

        // Check that the parameters are found within the assumed error bars.
View Full Code Here
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
    @Test
    public void testTrivial() {
        LinearProblem problem
            = new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum =
            optimizer.optimize(new MaxEval(100),
                               problem.getModelFunction(),
                               problem.getModelFunctionJacobian(),
                               problem.getTarget(),
                               new Weight(new double[] { 1 }),
                               new InitialGuess(new double[] { 0 }));
        Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
        Assert.assertEquals(1.5, optimum.getPoint()[0], 1e-10);
        Assert.assertEquals(3.0, optimum.getValue()[0], 1e-10);
    }
View Full Code Here
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
        LinearProblem problem
            = new LinearProblem(new double[][] { { 1, -1 }, { 0, 2 }, { 1, -2 } },
                                new double[] { 4, 6, 1 });

        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        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 }));
        Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
        Assert.assertEquals(7, optimum.getPoint()[0], 1e-10);
        Assert.assertEquals(3, optimum.getPoint()[1], 1e-10);
        Assert.assertEquals(4, optimum.getValue()[0], 1e-10);
        Assert.assertEquals(6, optimum.getValue()[1], 1e-10);
        Assert.assertEquals(1, optimum.getValue()[2], 1e-10);
    }
View Full Code Here
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
                { 0, 0, 0, 2, 0, 0 },
                { 0, 0, 0, 0, 2, 0 },
                { 0, 0, 0, 0, 0, 2 }
        }, new double[] { 0, 1.1, 2.2, 3.3, 4.4, 5.5 });
        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum =
            optimizer.optimize(new MaxEval(100),
                               problem.getModelFunction(),
                               problem.getModelFunctionJacobian(),
                               problem.getTarget(),
                               new Weight(new double[] { 1, 1, 1, 1, 1, 1 }),
                               new InitialGuess(new double[] { 0, 0, 0, 0, 0, 0 }));
        Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
        for (int i = 0; i < problem.target.length; ++i) {
            Assert.assertEquals(0.55 * i, optimum.getPoint()[i], 1e-10);
        }
    }
View Full Code Here
TOP

Related Classes of org.apache.commons.math3.optim.PointVectorValuePair$DataTransferObject

  • org.apache.commons.math3.analysis.differentiation.DerivativeStructure
  • org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction
  • org.apache.commons.math3.analysis.function.HarmonicOscillator
  • org.apache.commons.math3.analysis.function.Sin
  • org.apache.commons.math3.analysis.function.Sinc
  • org.apache.commons.math3.analysis.MultivariateFunction
  • org.apache.commons.math3.analysis.QuinticFunction
  • org.apache.commons.math3.analysis.solvers.BrentSolver
  • org.apache.commons.math3.analysis.UnivariateFunction
  • org.apache.commons.math3.complex.Complex
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.