public Class1()
{
var mlContext = new MLContext();
List <TrainFace> trainFaceData = MongoDBApi.MongoDBClient.Current.GetAllDataNormalisedFromTable("glasses");
var dataView = mlContext.Data.LoadFromEnumerable(trainFaceData);
var features = dataView.Schema.Select(col => col.Name).Where(colName => colName != "HairColor" && colName != "Label").ToArray();
var dataProcessPipeline = mlContext.Transforms.Text.FeaturizeText(outputColumnName: "HairColor", inputColumnName: nameof(TrainFace.HairColor))
.Append(mlContext.Transforms.Concatenate("Features", features));
var preppedData = dataProcessPipeline.Fit(dataView);
var trainer = mlContext.BinaryClassification.Trainers.SdcaLogisticRegression(labelColumnName: "Label", featureColumnName: "Features");
var pipeline = dataProcessPipeline.Append(trainer);
var model = pipeline.Fit(dataView);
var predictions = model.Transform(dataView);
var predictionFunc = mlContext.Model.CreatePredictionEngine <TrainFace, SwipePrediction>(model);
var trainFace = new TrainFace(ArresFace.GetArresFace());
var swipePrediction = predictionFunc.Predict(trainFace);
Console.WriteLine("SwipeDirection");
Console.WriteLine(swipePrediction.SwipeRight);
}
public List <TrainFace> GetAllDataNormalisedFromTable(string tableName)
{
var faces = GetAllDataFromTable(tableName);
List <TrainFace> trainFaces = new List <TrainFace>();
foreach (var face in faces)
{
var trainFace = new TrainFace(face);
trainFace.Normalize();
trainFaces.Add(trainFace);
}
return(trainFaces);
}
