private static List <int> ReadFeatureVectors(StreamReader reader)
{
string line;
List <int> feature_vectors = new List <int>();
while ((line = reader.ReadLine()) != null)
{
if (!line.StartsWith("#"))
{
Match label_match = new Regex(@"^(?<label>[+-]?\d+([.]\d+)?)(\s|$)").Match(line);
Debug.Assert(label_match.Success);
int label = Convert.ToInt32(label_match.Result("${label}"));
Match match = new Regex(@"(?<feature>\d+):(?<weight>[-]?[\d\.]+)").Match(line);
List <int> features = new List <int>();
List <float> weights = new List <float>();
while (match.Success)
{
int feature = Convert.ToInt32(match.Result("${feature}"));
float weight = Convert.ToSingle(match.Result("${weight}"), System.Globalization.CultureInfo.InvariantCulture);
match = match.NextMatch();
features.Add(feature);
weights.Add(weight);
}
int vec_id = SvmLightLib.NewFeatureVector(features.Count, features.ToArray(), weights.ToArray(), label);
feature_vectors.Add(vec_id);
}
}
return(feature_vectors);
}
public static void Main(string[] args)
{
// *** Test SVM^light inductive mode ***
Console.WriteLine("Testing SVM^light inductive mode ...");
Console.WriteLine("Training ...");
StreamReader reader = new StreamReader(@"..\..\..\Examples\Inductive\train.dat");
List <int> train_set = ReadFeatureVectors(reader);
reader.Close();
int model_id = SvmLightLib.TrainModel("", train_set.Count, train_set.ToArray());
Console.WriteLine("Classifying ...");
reader = new StreamReader(@"..\..\..\Examples\Inductive\test.dat");
List <int> test_set = ReadFeatureVectors(reader);
reader.Close();
int correct = 0;
foreach (int vec_id in test_set)
{
int true_lbl = (int)SvmLightLib.GetFeatureVectorLabel(vec_id);
SvmLightLib.Classify(model_id, 1, new int[] { vec_id });
Debug.Assert(SvmLightLib.GetFeatureVectorClassifScoreCount(vec_id) == 1);
double result = SvmLightLib.GetFeatureVectorClassifScore(vec_id, 0);
int predicted_lbl = result > 0 ? 1 : -1;
if (true_lbl == predicted_lbl)
{
correct++;
}
}
Console.WriteLine("Accuracy: {0:0.00}%", (double)correct / (double)test_set.Count * 100.0);
// cleanup
SvmLightLib.DeleteModel(model_id);
foreach (int vec_id in train_set)
{
SvmLightLib.DeleteFeatureVector(vec_id);
}
foreach (int vec_id in test_set)
{
SvmLightLib.DeleteFeatureVector(vec_id);
}
// *** Test SVM^light transductive mode ***
Console.WriteLine("Testing SVM^light transductive mode ...");
Console.WriteLine("Training ...");
reader = new StreamReader(@"..\..\..\Examples\Transductive\train_transduction.dat");
train_set = ReadFeatureVectors(reader);
reader.Close();
model_id = SvmLightLib.TrainModel("", train_set.Count, train_set.ToArray());
Console.WriteLine("Classifying ...");
reader = new StreamReader(@"..\..\..\Examples\Transductive\test.dat");
test_set = ReadFeatureVectors(reader);
reader.Close();
correct = 0;
foreach (int vec_id in test_set)
{
int true_lbl = (int)SvmLightLib.GetFeatureVectorLabel(vec_id);
SvmLightLib.Classify(model_id, 1, new int[] { vec_id });
Debug.Assert(SvmLightLib.GetFeatureVectorClassifScoreCount(vec_id) == 1);
double result = SvmLightLib.GetFeatureVectorClassifScore(vec_id, 0);
int predicted_lbl = result > 0 ? 1 : -1;
if (true_lbl == predicted_lbl)
{
correct++;
}
}
Console.WriteLine("Accuracy: {0:0.00}%", (double)correct / (double)test_set.Count * 100.0);
// cleanup
SvmLightLib.DeleteModel(model_id);
foreach (int vec_id in train_set)
{
SvmLightLib.DeleteFeatureVector(vec_id);
}
foreach (int vec_id in test_set)
{
SvmLightLib.DeleteFeatureVector(vec_id);
}
// *** Test SVM^multiclass ***
Console.WriteLine("Testing SVM^multiclass ...");
Console.WriteLine("Training ...");
reader = new StreamReader(@"..\..\..\Examples\Multiclass\train.dat");
train_set = ReadFeatureVectors(reader);
reader.Close();
model_id = SvmLightLib.TrainMulticlassModel("-c 5000", train_set.Count, train_set.ToArray());
Console.WriteLine("Classifying ...");
reader = new StreamReader(@"..\..\..\Examples\Multiclass\test.dat");
test_set = ReadFeatureVectors(reader);
reader.Close();
correct = 0;
foreach (int vec_id in test_set)
{
int true_lbl = (int)SvmLightLib.GetFeatureVectorLabel(vec_id);
SvmLightLib.MulticlassClassify(model_id, 1, new int[] { vec_id });
int n = SvmLightLib.GetFeatureVectorClassifScoreCount(vec_id);
double max_score = double.MinValue;
int predicted_lbl = -1;
for (int i = 0; i < n; i++)
{
double score = SvmLightLib.GetFeatureVectorClassifScore(vec_id, i);
if (score > max_score)
{
max_score = score; predicted_lbl = i + 1;
}
}
if (true_lbl == predicted_lbl)
{
correct++;
}
}
Console.WriteLine("Accuracy: {0:0.00}%", (double)correct / (double)test_set.Count * 100.0);
// cleanup
SvmLightLib.DeleteMulticlassModel(model_id);
foreach (int vec_id in train_set)
{
SvmLightLib.DeleteFeatureVector(vec_id);
}
foreach (int vec_id in test_set)
{
SvmLightLib.DeleteFeatureVector(vec_id);
}
}
