Esempio n. 1
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        public void testLikelihoodWeighting_AIMA3e_pg533()
        {
            // AIMA3e pg. 533
            // <b>P</b>(Rain | Cloudy = true, WetGrass = true)
            IBayesianNetwork bn = BayesNetExampleFactory.constructCloudySprinklerRainWetGrassNetwork();

            AssignmentProposition[] e = new AssignmentProposition[] {
                new AssignmentProposition(ExampleRV.CLOUDY_RV, true),
                new AssignmentProposition(ExampleRV.WET_GRASS_RV, true)
            };
            // sample P(Sprinkler | Cloudy = true) = <0.1, 0.9>; suppose
            // Sprinkler=false
            // sample P(Rain | Cloudy = true) = <0.8, 0.2>; suppose Rain=true
            MockRandomizer r = new MockRandomizer(new double[] { 0.5, 0.5 });

            LikelihoodWeighting lw = new LikelihoodWeighting(r);

            double[] estimate = lw.likelihoodWeighting(
                new IRandomVariable[] { ExampleRV.RAIN_RV }, e, bn, 1)
                                .getValues();

            // Here the event [true,false,true,true] should have weight 0.45,
            // and this is tallied under Rain = true, which when normalized
            // should be <1.0, 0.0>;
            assertArrayEquals(new double[] { 1.0, 0.0 }, estimate, DELTA_THRESHOLD);
        }
Esempio n. 2
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        public void testLikelihoodWeighting_basic()
        {
            IBayesianNetwork bn = BayesNetExampleFactory.constructCloudySprinklerRainWetGrassNetwork();

            AssignmentProposition[] e = new AssignmentProposition[] { new AssignmentProposition(ExampleRV.SPRINKLER_RV, true) };
            MockRandomizer          r = new MockRandomizer(new double[] { 0.5, 0.5, 0.5, 0.5 });

            LikelihoodWeighting lw = new LikelihoodWeighting(r);

            double[] estimate = lw.likelihoodWeighting(
                new IRandomVariable[] { ExampleRV.RAIN_RV }, e, bn, 1000)
                                .getValues();

            assertArrayEquals(new double[] { 1.0, 0.0 }, estimate, DELTA_THRESHOLD);
        }