1 write to Mean
Microsoft.ML.Data (1)
Transforms\Normalizer.cs (1)
928Mean = mean;
18 references to Mean
Microsoft.ML.Data (1)
Transforms\Normalizer.cs (1)
894/// The cumulative density function is parameterized by <see cref="CdfNormalizerModelParameters{TData}.Mean"/> and
Microsoft.ML.Samples (3)
Dynamic\Transforms\NormalizeLogMeanVariance.cs (1)
78.Mean[1] + ") / (" + transformParams.StandardDeviation[1] +
Dynamic\Transforms\NormalizeLogMeanVarianceFixZero.cs (1)
62Console.WriteLine($"y = 0.5* (1 + ERF((Math.Log(x)- {transformParams.Mean[1]}) / ({transformParams.StandardDeviation[1]} * sqrt(2)))");
Dynamic\Transforms\NormalizeMeanVariance.cs (1)
78Console.WriteLine(" y = 0.5* (1 + ERF((x- " + transformParams.Mean[1] +
Microsoft.ML.Tests (14)
Transformers\NormalizerTests.cs (14)
196Assert.Equal(1.75623953f, floatCdfLogMeanData.Mean); 201Assert.Equal(4, floatCdfLogMeanDataVec.Mean.Length); 206Assert.Equal(1.7562395401953814, doubleCdfLogMeanData.Mean); 211Assert.Equal(4, doubleCdfLogMeanDataVec.Mean.Length); 330Assert.Equal(-0.310623198747635f, floatCdfLogMeanModel.Mean); 335Assert.Equal(4, floatCdfLogMeanModelVector.Mean.Length); 336Assert.True(-0.3106232f == floatCdfLogMeanModelVector.Mean[0]); 337Assert.True(-1.08362031f == floatCdfLogMeanModelVector.Mean[3]); 344Assert.Equal(-0.31062321927759518, doubleCdfLogMeanModel.Mean); 349Assert.Equal(4, doubleCdfLogMeanModelVector.Mean.Length); 350Assert.True(-0.31062321927759518 == doubleCdfLogMeanModelVector.Mean[0]); 351Assert.True(-1.0836203140680853 == doubleCdfLogMeanModelVector.Mean[3]); 923Assert.NotEqual(0f, transformParams.Mean); 966Assert.NotEqual(0f, transformParams.Mean[i]);