Package org.apache.mahout.math.jet.random

Examples of org.apache.mahout.math.jet.random.Uniform.nextDouble()

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            @Override
            public double nextDouble() {
                double x;
                if (gen.nextDouble() < 0.5) {
                    x = uniform.nextDouble();
                } else {
                    x = normal.nextDouble();
                }
                return x;
            }
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            @Override
            public double nextDouble() {
                double x;
                if (gen.nextDouble() < 0.5) {
                    x = uniform.nextDouble();
                } else {
                    x = normal.nextDouble();
                }
                return x;
            }
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            @Override
            public double nextDouble() {
                double x;
                if (gen.nextDouble() < 0.5) {
                    x = uniform.nextDouble();
                } else {
                    x = normal.nextDouble();
                }
                return x;
            }
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            @Override
            public double nextDouble() {
                double x;
                if (gen.nextDouble() < 0.5) {
                    x = uniform.nextDouble();
                } else {
                    x = normal.nextDouble();
                }
                return x;
            }
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            @Override
            public double nextDouble() {
                double x;
                if (gen.nextDouble() < 0.5) {
                    x = uniform.nextDouble();
                } else {
                    x = normal.nextDouble();
                }
                return x;
            }
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        }

        double[] prob = new double[totalItems];
        Uniform u = new Uniform(gen);
        for (int j = 0; j < totalItems; j++) {
            prob[j] = u.nextDouble();
        }
        Arrays.sort(prob);
        for (int j = 0; j < totalItems; j++) {
            prob[j] = 1 - prob[j];
        }
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                    regret += prob[j] - prob[k];
                }

                for (int j = 0; j < pageSize; j++) {
                    int k = page.get(j);
                    int reward = u.nextDouble() < prob[k] ? 1 : 0;
                    bandit.get(m).train(k, reward);
                }
            }
            precision /= keyItems * 50.0;
            regret /= 50;
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