public static IrisApiViewModel GetClassification()
{
IrisApiViewModel iavmodel = new IrisApiViewModel
{
ResultFlower = ""
};
// STEP 2: Create a ML.NET environment
var mlContext = new MLContext();
// If working in Visual Studio, make sure the 'Copy to Output Directory'
// property of iris-data.txt is set to 'Copy always'
string dataPath = "iris-data.txt";
var reader = mlContext.Data.TextReader(new TextLoader.Arguments()
{
Separator = ",",
HasHeader = true,
Column = new[]
{
new TextLoader.Column("SepalLength", DataKind.R4, 0),
new TextLoader.Column("SepalWidth", DataKind.R4, 1),
new TextLoader.Column("PetalLength", DataKind.R4, 2),
new TextLoader.Column("PetalWidth", DataKind.R4, 3),
new TextLoader.Column("Label", DataKind.Text, 4)
}
});
IDataView trainingDataView = reader.Read(new MultiFileSource(dataPath));
// STEP 3: Transform your data and add a learner
// Assign numeric values to text in the "Label" column, because only
// numbers can be processed during model training.
// Add a learning algorithm to the pipeline. e.g.(What type of iris is this?)
// Convert the Label back into original text (after converting to number in step 3)
var pipeline = mlContext.Transforms.Categorical.MapValueToKey("Label")
.Append(mlContext.Transforms.Concatenate("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"))
.Append(mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(label: "Label", features: "Features"))
.Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
// STEP 4: Train your model based on the data set
var model = pipeline.Fit(trainingDataView);
// STEP 5: Use your model to make a prediction
// You can change these numbers to test different predictions
var prediction = model.MakePredictionFunction <IrisData, IrisPrediction>(mlContext).Predict(
new IrisData()
{
SepalLength = 3.3f,
SepalWidth = 1.6f,
PetalLength = 0.2f,
PetalWidth = 5.1f,
});
iavmodel.ResultFlower = prediction.PredictedLabels;
return(iavmodel);
}
public ActionResult Jam()
{
IrisApiViewModel iavm = Ml.Core.Iris.GetClassification();
IrisViewModel irm = new IrisViewModel
{
ResultFlower = iavm.ResultFlower
};
return(View(iavm));
}