private void UpdateTextBox()
{
using (MemoryStream s = new MemoryStream()) {
StreamWriter writer = new StreamWriter(s);
writer.WriteLine("RangeTransform:");
writer.Flush();
using (MemoryStream memStream = new MemoryStream()) {
RangeTransform.Write(memStream, Content.RangeTransform);
memStream.Seek(0, SeekOrigin.Begin);
memStream.WriteTo(s);
}
writer.WriteLine("Model:");
writer.Flush();
using (MemoryStream memStream = new MemoryStream()) {
svm.svm_save_model(new StreamWriter(memStream), Content.Model);
memStream.Seek(0, SeekOrigin.Begin);
memStream.WriteTo(s);
}
s.Flush();
s.Seek(0, SeekOrigin.Begin);
StreamReader reader = new StreamReader(s);
textBox.Text = reader.ReadToEnd();
}
}
public string SaveModel(ClassifyOptions options)
{
options.TransformFilePath = Path.Combine(options.ModelDir, "transform");
options.FeaturesFileName = Path.Combine(options.ModelDir, "features");
options.DictionaryFileName = Path.Combine(options.ModelDir, "dictionary");
options.CategoriesFileName = Path.Combine(options.ModelDir, "categories");
File.WriteAllText(options.FeaturesFileName, JsonConvert.SerializeObject(features));
File.WriteAllText(options.DictionaryFileName, JsonConvert.SerializeObject(dictionary));
File.WriteAllText(options.CategoriesFileName, JsonConvert.SerializeObject(categories));
RangeTransform.Write(options.TransformFilePath, transform);
Bigtree.Algorithm.SVM.Model.Write(options.ModelFilePath, model);
return(options.ModelFilePath);
}
public void SVMClassifierTrain(List <FeaturesWithLabel> featureSets, ClassifyOptions options, SvmType svm = SvmType.C_SVC, KernelType kernel = KernelType.RBF, bool probability = true, string outputFile = null)
{
// copy test multiclass Model
Problem train = new Problem();
train.X = GetData(featureSets).ToArray();
train.Y = GetLabels(featureSets).ToArray();
train.Count = train.X.Count();
train.MaxIndex = 300;//int.MaxValue;
Parameter param = new Parameter();
RangeTransform transform = RangeTransform.Compute(train);
Problem scaled = transform.Scale(train);
param.Gamma = 1.0 / 3;
param.SvmType = svm;
param.KernelType = kernel;
param.Probability = probability;
int numberOfClasses = train.Y.Distinct().Count();
if (numberOfClasses == 1)
{
throw new ArgumentException("Number of classes can't be one!");
}
if (svm == SvmType.C_SVC)
{
for (int i = 0; i < numberOfClasses; i++)
{
param.Weights[i] = 1;
}
}
var model = Training.Train(scaled, param);
RangeTransform.Write(options.TransformFilePath, transform);
SVM.BotSharp.MachineLearning.Model.Write(options.ModelFilePath, model);
Console.Write("Training finished!");
}
