public void PredictCustomerClusters()
{
//var data = _mlContext.Data.LoadFromTextFile(path: _pivotDataLocation,
// columns: new[]
// {
// new TextLoader.Column("Features", DataKind.Single, new[] {new TextLoader.Range(0, 31) }),
// new TextLoader.Column(nameof(PivotData.LastName), DataKind.String, 32)
// },
// hasHeader: true,
// separatorChar: ',');
var predEngine = _mlContext.Model.CreatePredictionEngine <ModelInput, ClusteringPrediction>(_trainedModel);
//Load sample data for prediction
var data = new ModelInput
{
Features = new float[] { 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 },
LastName = "Mera"
};
// Try model on sample data
ClusteringPrediction result = predEngine.Predict(data);
List <ClusteringPrediction> enumResult = new List <ClusteringPrediction>
{
result
};
Console.WriteLine($"\nCustomer: {data.LastName} | Prediction: {result.SelectedClusterId} cluster");
Console.WriteLine($"Location X: {result.Location[0].ToString()} | Location Y: {result.Location[1].ToString()} ");
SaveCustomerSegmentationPlotChartSingle(enumResult.ToArray(), "customerSegmentation2.svg");
OpenChartInDefaultWindow("customerSegmentation2.svg");
}
internal static Candidate FromPrediction(ClusteringPrediction cp)
{
return(new Candidate
{
UserId = cp.UserId,
Score = cp.Distance[cp.SelectedClusterId]
});
}
