private void Rm_DataStreamUpdate(string dataStream, string preprocessedDataStream)
{
var stream = dataStream.Split(",");
var preprocessedStream = preprocessedDataStream.Split(",");
if (isPrediction)
{
string pred = "";
var values = preprocessedDataStream.Split(',');
for (int i = 0; i < values.Length - 1; i++)
{
dataVals.Add(float.Parse(values[i]));
}
if (dataVals.Count == 12300)
{
PredictionEngine.InputData input = new PredictionEngine.InputData()
{
PixelValues = dataVals.ToArray()
};
pred = preditionEngine.PredictSingle(input);
Invoke(new AppendTextOnTextBox(SetPredictionLabel), new object[] { pred });
dataVals.Clear();
}
else if (dataVals.Count > 12420)
{
dataVals.Clear();
}
if (isEvaluating && (pred != null))
{
CountPredition(pred);
Invoke(new AppendTextOnTextBox(SetText), new object[] { EvaluationText() });
}
else
{
Invoke(new AppendTextOnTextBox(SetText), new object[] { pred });
}
}
else
{
Invoke(new ShowDataStream(ShowDataStreamOnTextBox), new object[] { "raw_data", dataStream });
Invoke(new ShowDataStream(ShowPreprocessedData), new object[] { "preprocessed_data", preprocessedDataStream });
}
}
private static void DoEvaluation()
{
string title = "Light GBM";
ISignPreditionEngine preditionEngine = new MLNetHierarchicalSignPrediciton(MLNetClassifier.LightGBM);
float[] input_feature = new float[12300];
string[] lines = File.ReadAllLines(Environment.CurrentDirectory + "\\SignTestData.csv");
int count = 0;
int totalPred = 0;
foreach (string line in lines)
{
var input = line.Split(',');
var output = input[input.Length - 1];
for (int i = 0; i < input.Length - 1; i++)
{
input_feature[i] = float.Parse(input[i]);
}
var inputData = new PredictionEngine.InputData {
PixelValues = input_feature
};
var pred = preditionEngine.PredictSingle(inputData);
if (pred != null)
{
if (pred.ToLower().Equals(output.ToLower()))
{
count++;
}
Console.WriteLine($"Real : {output} ; Pred : {pred}");
totalPred++;
}
}
var accuracy = (float)count / totalPred;
Console.WriteLine("====================================");
Console.WriteLine($"Evaluation for {title}");
Console.WriteLine("====================================");
Console.WriteLine($"Accuracy : {accuracy}");
}
private void Rm_DataStreamUpdate(string dataStream, string preprocessedDataStream)
{
var values = preprocessedDataStream.Split(',');
for (int i = 0; i < values.Length - 1; i++)
{
dataVals.Add(float.Parse(values[i]));
}
if (dataVals.Count == 12300)
{
PredictionEngine.InputData input = new PredictionEngine.InputData()
{
PixelValues = dataVals.ToArray()
};
var pred = preditionEngine.Predict(input);
//Invoke(new AppendTextOnTextBox(AppendText), new object[] { pred });
Dispatcher.Invoke(new AppendTextOnTextBox(SetText), new object[] { pred });
dataVals.Clear();
}
}