class Program
{
    static void Main(string[] args)
    {
        var trainData = new[] { new TestData() { data = "male" }, new TestData() { data = "female"} };
        var testData = new[] { new TestData() { data = "male" }, new TestData() { data = "female" }, new TestData() { data = "fem" }, new TestData() { data = "fem1" } };

        var mlContext = new MLContext();
        var trainDataView = mlContext.Data.LoadFromEnumerable(trainData);
        var testDataView = mlContext.Data.LoadFromEnumerable(testData);

        var pipe = mlContext.Transforms.Categorical.OneHotEncoding("Bag", "data", OneHotEncodingTransformer.OutputKind.Bag)
            .Append(mlContext.Transforms.Categorical.OneHotEncoding("Key", "data", OneHotEncodingTransformer.OutputKind.Key))
            .Append(mlContext.Transforms.Categorical.OneHotEncoding("Ind", "data", OneHotEncodingTransformer.OutputKind.Ind))
            .Append(mlContext.Transforms.Categorical.OneHotEncoding("Bin", "data", OneHotEncodingTransformer.OutputKind.Bin));

        var transformer = pipe.Fit(trainDataView);

        var result = transformer.Transform(testDataView);

        var bags = result.GetColumn<float[]>(mlContext, "Bag").ToArray();
        var inds = result.GetColumn<float[]>(mlContext, "Ind").ToArray();
        var bins = result.GetColumn<float[]>(mlContext, "Bin").ToArray();

        Console.WriteLine("Number of dimensions (should be 2) in each output kind: {0} {1} {2}", bags[0].Length, inds[0].Length, bins[0].Length);
        // Number of dimensions (should be 2) in each output kind: 2 2 3
    }
}