private ClusterPrediction PredictIris(float SepalLength, float SepalWidth, float PetalLength, float PetalWidth)
{
IrisData iris = new IrisData
{
SepalLength = SepalLength,
SepalWidth = SepalWidth,
PetalLength = PetalLength,
PetalWidth = PetalWidth
};
return(PredictIris(iris));
}
private void btnPredict_Click(object sender, EventArgs e)
{
if (Input == null)
{
Input = new IrisData();
}
Input.PetalLength = float.Parse(txtPtlLgth.Text);
Input.PetalWidth = float.Parse(txtPtlWdth.Text);
Input.SepalLength = float.Parse(txtSplLgth.Text);
Input.SepalWidth = float.Parse(txtSplWdth.Text);
ClusterPrediction prediction = PredictIris(Input);
txtPredictions.Text += $"\r\n\r\nPetal Length: {txtPtlLgth.Text}cm";
txtPredictions.Text += $"\r\nPetal Width: {txtPtlWdth.Text}cm";
txtPredictions.Text += $"\r\nSepal Length: {txtSplLgth.Text}cm";
txtPredictions.Text += $"\r\nSepal Width: {txtSplWdth.Text}cm";
txtPredictions.Text += $"\r\n{new string('-', 30)}";
txtPredictions.Text += $"\r\nCluster: {prediction.PredictedClusterId}";
txtPredictions.Text += $"\r\nDistances: {string.Join(" ", prediction.Distances)}";
txtPredictions.Text += $"\r\n{new string('*', 50)}";
}
private void Main_Load(object sender, EventArgs e)
{
Input = new IrisData();
ClusterPredictions = new List <ClusterPrediction>();
TrainedData = new List <IrisData>();
}
private ClusterPrediction PredictIris(IrisData iris)
{
return(Model.Predict(iris));
}
