public void TestEvaluate() 
        {
		    DataModel model = GetDataModel();
		    RecommenderBuilder builder = new SlopeOneRecommenderBuilder();
		    RecommenderEvaluator evaluator = new RMSRecommenderEvaluator();
		    double eval = evaluator.Evaluate(builder, model, 0.75, 1.0);
		    Assert.AreEqual(0.26387685767414826, eval, EPSILON);
	    }
        public void TestEvaluate()
        {
            DataModel            model     = GetDataModel();
            RecommenderBuilder   builder   = new SlopeOneRecommenderBuilder();
            RecommenderEvaluator evaluator = new RMSRecommenderEvaluator();
            double eval = evaluator.Evaluate(builder, model, 0.75, 1.0);

            Assert.AreEqual(0.26387685767414826, eval, EPSILON);
        }
	    public void TestEvaluate()  
	    {
		    DataModel model = GetDataModel();
		    RecommenderBuilder builder = new SlopeOneRecommenderBuilder();
		    RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();
		    IRStatistics stats = evaluator.Evaluate(builder, model, 5, 0.2, 1.0);
		    Assert.IsNotNull(stats);
		    Assert.AreEqual(0.2, stats.Precision, EPSILON);
		    Assert.AreEqual(1.0, stats.Recall, EPSILON);
		    Assert.AreEqual(0.33333, stats.GetF1Measure(), EPSILON);
	    }
예제 #4
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        public void TestEvaluate()
        {
            DataModel                   model     = GetDataModel();
            RecommenderBuilder          builder   = new SlopeOneRecommenderBuilder();
            RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();
            IRStatistics                stats     = evaluator.Evaluate(builder, model, 5, 0.2, 1.0);

            Assert.IsNotNull(stats);
            Assert.AreEqual(0.2, stats.Precision, EPSILON);
            Assert.AreEqual(1.0, stats.Recall, EPSILON);
            Assert.AreEqual(0.33333, stats.GetF1Measure(), EPSILON);
        }