Package org.apache.commons.math3.ml.distance

Examples of org.apache.commons.math3.ml.distance.DistanceMeasure.compute()

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        final double[] features = new double[] { 0.3 };
        final double[] distancesBefore = new double[netSize];
        int count = 0;
        for (Neuron n : net) {
            distancesBefore[count++] = dist.compute(n.getFeatures(), features);
        }
        final Neuron bestBefore = MapUtils.findBest(features, net, dist);

        // Initial distance from the best match is larger than zero.
        Assert.assertTrue(dist.compute(bestBefore.getFeatures(), features) >= 0.2);
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            distancesBefore[count++] = dist.compute(n.getFeatures(), features);
        }
        final Neuron bestBefore = MapUtils.findBest(features, net, dist);

        // Initial distance from the best match is larger than zero.
        Assert.assertTrue(dist.compute(bestBefore.getFeatures(), features) >= 0.2);

        update.update(net, features);

        final double[] distancesAfter = new double[netSize];
        count = 0;
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        update.update(net, features);

        final double[] distancesAfter = new double[netSize];
        count = 0;
        for (Neuron n : net) {
            distancesAfter[count++] = dist.compute(n.getFeatures(), features);
        }
        final Neuron bestAfter = MapUtils.findBest(features, net, dist);

        Assert.assertEquals(bestBefore, bestAfter);
        // Distance is now zero.
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        }
        final Neuron bestAfter = MapUtils.findBest(features, net, dist);

        Assert.assertEquals(bestBefore, bestAfter);
        // Distance is now zero.
        Assert.assertEquals(0, dist.compute(bestAfter.getFeatures(), features), 0d);

        for (int i = 0; i < netSize; i++) {
            // All distances have decreased.
            Assert.assertTrue(distancesAfter[i] < distancesBefore[i]);
        }
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        final double[] features = new double[] { 0.3 };
        final double[] distancesBefore = new double[netSize];
        int count = 0;
        for (Neuron n : net) {
            distancesBefore[count++] = dist.compute(n.getFeatures(), features);
        }
        final Neuron bestBefore = MapUtils.findBest(features, net, dist);

        // Initial distance from the best match is larger than zero.
        Assert.assertTrue(dist.compute(bestBefore.getFeatures(), features) >= 0.2);
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            distancesBefore[count++] = dist.compute(n.getFeatures(), features);
        }
        final Neuron bestBefore = MapUtils.findBest(features, net, dist);

        // Initial distance from the best match is larger than zero.
        Assert.assertTrue(dist.compute(bestBefore.getFeatures(), features) >= 0.2);

        update.update(net, features);

        final double[] distancesAfter = new double[netSize];
        count = 0;
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        update.update(net, features);

        final double[] distancesAfter = new double[netSize];
        count = 0;
        for (Neuron n : net) {
            distancesAfter[count++] = dist.compute(n.getFeatures(), features);
        }
        final Neuron bestAfter = MapUtils.findBest(features, net, dist);

        Assert.assertEquals(bestBefore, bestAfter);
        // Distance is now zero.
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        }
        final Neuron bestAfter = MapUtils.findBest(features, net, dist);

        Assert.assertEquals(bestBefore, bestAfter);
        // Distance is now zero.
        Assert.assertEquals(0, dist.compute(bestAfter.getFeatures(), features), 0d);

        for (int i = 0; i < netSize; i++) {
            // All distances have decreased.
            Assert.assertTrue(distancesAfter[i] < distancesBefore[i]);
        }
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