Package org.apache.commons.math3.distribution

Examples of org.apache.commons.math3.distribution.WeibullDistribution.sample()

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        final double tol = 224;
        for (int i = 0; i < sz; i++) {
            x = distX.sample();
            for (int j = 0; j < sz; j++) {
                y = distY.sample();
//                 System.out.println(x + " " + y + " " + f.value(x, y) + " " + bcf.value(x, y));
                Assert.assertEquals(f.value(x, y),  bcf.value(x, y), tol);
            }
//             System.out.println();
        }
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        for ( int i = 0; i < numSamples; i++ )
        {
            x = distX.sample();
            for ( int j = 0; j < numSamples; j++ )
            {
                y = distY.sample();
//                 System.out.println(x + " " + y + " " + f.value(x, y) + " " + p.value(x, y));
                Assert.assertEquals(f.value(x, y),  p.value(x, y), tol);
            }
//             System.out.println();
        }
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        for ( int i = 0; i < numSamples; i++ )
        {
            x = distX.sample();
            for ( int j = 0; j < numSamples; j++ )
            {
                y = distY.sample();
//                 System.out.println(x + " " + y + " " + f.value(x, y) + " " + p.value(x, y));
                Assert.assertEquals(f.value(x, y),  p.value(x, y), tol);
            }
//             System.out.println();
        }
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            new UniformRealDistribution( rng, xValues[0], xValues[xValues.length - 1] );

        double sumError = 0;
        for ( int i = 0; i < numberOfSamples; i++ )
        {
            currentX = distX.sample();
            expected = f.value( currentX );
            actual = interpolation.value( currentX );
            sumError += FastMath.abs( actual - expected );
            assertEquals( expected, actual, maxTolerance );
        }
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        RandomDataGenerator random = new RandomDataGenerator();

        // Generate 10 distinct random values
        for (int i = 0; i < 10; i++) {
            final RealDistribution u = new UniformRealDistribution(i + 0.5, i + 0.75);
            original[i] = u.sample();
        }

        // Generate a random permutation, making sure it is not the identity
        boolean isIdentity = true;
        do {
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        final double[] coeff = { 12.9, -3.4, 2.1 }; // 12.9 - 3.4 x + 2.1 x^2
        final PolynomialFunction f = new PolynomialFunction(coeff);

        // Collect data from a known polynomial.
        for (int i = 0; i < 100; i++) {
            final double x = rng.sample();
            fitter.addObservedPoint(x, f.value(x));
        }

        // Start fit from initial guesses that are far from the optimal values.
        final double[] best = fitter.fit(new double[] { -1e-20, 3e15, -5e25 });
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        final double[] coeff = { 12.9, -3.4, 2.1 }; // 12.9 - 3.4 x + 2.1 x^2
        final PolynomialFunction f = new PolynomialFunction(coeff);

        // Collect data from a known polynomial.
        for (int i = 0; i < 100; i++) {
            final double x = rng.sample();
            fitter.addObservedPoint(x, f.value(x));
        }

        // Start fit from initial guesses that are far from the optimal values.
        final double[] best = fitter.fit(new double[] { -1e-20, 3e15, -5e25 });
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        final PolynomialFunction f = new PolynomialFunction(coeff);

        // Collect data from a known polynomial.
        final WeightedObservedPoints obs = new WeightedObservedPoints();
        for (int i = 0; i < 100; i++) {
            final double x = rng.sample();
            obs.add(x, f.value(x));
        }

        // Start fit from initial guesses that are far from the optimal values.
        final PolynomialCurveFitter fitter
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        double sumError = 0;
        for ( int i = 0; i < numberOfSamples; i++ )
        {
            currentX = distX.sample();
            currentY = distY.sample();
            expected = f.value( currentX, currentY );
            actual = interpolation.value( currentX, currentY );
            sumError += FastMath.abs( actual - expected );
            assertEquals( expected, actual, maxTolerance );
    }
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        final int numSamples = 50;
        final double tol = 6;
        for (int i = 0; i < numSamples; i++) {
            x = distX.sample();
            for (int j = 0; j < numSamples; j++) {
                y = distY.sample();
//                 System.out.println(x + " " + y + " " + f.value(x, y) + " " + p.value(x, y));
                Assert.assertEquals(f.value(x, y),  p.value(x, y), tol);
            }
//             System.out.println();
        }
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