public void LogisticsRegression_Test_iterations_10_learningrate_013()
        {
            var         desc = loadMetaData();
            LearningApi api  = new LearningApi(desc);

            //Real dataset must be defined as object type, because data can be numeric, binary and classification
            api.UseActionModule <object[][], object[][]>((input, ctx) =>
            {
                return(loadRealDataSample());
            });

            // Use mapper for data, which will extract (map) required columns
            api.UseDefaultDataMapper();

            api.UseMinMaxNormalizer();

            //run logistic regression for 10 iterations with learningRate=0.13
            api.UseLogisticRegression(0.13, 10);

            api.Run();

            LogisticRegressionScore score = api.GetScore() as LogisticRegressionScore;

            //Errors during each iteration. IF the learningRate is suitable errors is describing for every next iteration
            Assert.Equal(Math.Round(score.Errors[0], 5), 0.24278);
            Assert.Equal(Math.Round(score.Errors[1], 5), 0.23749);
            Assert.Equal(Math.Round(score.Errors[2], 5), 0.23359);
            Assert.Equal(Math.Round(score.Errors[3], 5), 0.23010);
            Assert.Equal(Math.Round(score.Errors[4], 5), 0.22740);
            Assert.Equal(Math.Round(score.Errors[5], 5), 0.22476);
            Assert.Equal(Math.Round(score.Errors[6], 5), 0.22271);
            Assert.Equal(Math.Round(score.Errors[7], 5), 0.22065);
            Assert.Equal(Math.Round(score.Errors[8], 5), 0.21902);
            Assert.Equal(Math.Round(score.Errors[9], 5), 0.21739);

            //LG Model Best Found model in 10 iteration
            Assert.Equal(Math.Round(score.Weights[0], 5), 0.06494);
            Assert.Equal(Math.Round(score.Weights[1], 5), 0.21584);
            Assert.Equal(Math.Round(score.Weights[2], 5), 0.89901);
            Assert.Equal(Math.Round(score.Weights[3], 5), 0.51497);
            Assert.Equal(Math.Round(score.Weights[4], 5), -0.30213);
            Assert.Equal(Math.Round(score.Weights[5], 5), -0.30213);
            Assert.Equal(Math.Round(score.Weights[6], 5), -0.85624);


            //define data for testing (prediction)
            LearningApi apiPrediction = new LearningApi(desc);

            //Real dataset must be defined as object type, because data can be numeric, binary and classification
            apiPrediction.UseActionModule <object[][], object[][]>((input, ctx) =>
            {
                var data = new object[4][]
                {
                    new object[] { 0.202, "blue", "male", 13, "yes" },
                    new object[] { 0.447, "green", "female", 37, "no" },
                    new object[] { 0.120, "red", "male", "21", "yes" },
                    new object[] { 0.313, "green", "male", 22, "yes" },
                };
                return(data);
            });

            // Use mapper for data, which will extract (map) required columns
            apiPrediction.UseDefaultDataMapper();
            var testData = apiPrediction.Run();

            //use previous trained model
            var result = api.Algorithm.Predict(testData as double[][], api.Context) as LogisticRegressionResult;

            Assert.Equal(Math.Round(result.PredictedValues[0], 5), 1E-05);
            Assert.Equal(Math.Round(result.PredictedValues[1], 5), 0);
            Assert.Equal(Math.Round(result.PredictedValues[2], 5), 0);
            Assert.Equal(Math.Round(result.PredictedValues[3], 5), 0);
        }