public void MinMaxDeNormalization_test1() { // Creates learning api object LearningApi api = new LearningApi(loadMetaData1()); //Real dataset must be defined as object type, because data can be numeric, binary and classification api.UseActionModule <object[][], object[][]>((input, ctx) => { return(GetRealDataSample()); }); //this call must be first in the pipeline api.UseDefaultDataMapper(); // api.UseMinMaxNormalizer(); //use denormalizer on normalized data api.UseMinMaxDeNormalizer(); // var result = api.Run() as double[][]; //Test result for normalization var expected = GetTransformedNumericDataSample(); for (int i = 0; i < expected.Length; i++) { for (int j = 0; j < expected[0].Length; j++) { Assert.Equal(Math.Round(result[i][j], 4), expected[i][j]); } } // return; }