private static void TestSMO()
{
Console.WriteLine("Downloading dataset");
var news20 = new Accord.DataSets.News20(@"C:\Temp\");
Sparse <double>[] inputs = news20.Training.Item1.Get(0, 2000);
int[] outputs = news20.Training.Item2.ToMulticlass().Get(0, 2000);
var learn = new MultilabelSupportVectorLearning <Linear, Sparse <double> >()
{
// using LIBLINEAR's SVC dual for each SVM
Learner = (p) => new SequentialMinimalOptimization <Linear, Sparse <double> >()
{
Strategy = SelectionStrategy.SecondOrder,
Complexity = 1.0,
Tolerance = 1e-4,
CacheSize = 1000
},
};
Console.WriteLine("Learning");
Stopwatch sw = Stopwatch.StartNew();
var svm = learn.Learn(inputs, outputs);
Console.WriteLine(sw.Elapsed);
Console.WriteLine("Predicting");
sw = Stopwatch.StartNew();
int[] predicted = svm.ToMulticlass().Decide(inputs);
Console.WriteLine(sw.Elapsed);
var test = new ConfusionMatrix(predicted, outputs);
Console.WriteLine("Test acc: " + test.Accuracy);
}
private static void TestPredictSparseSVM()
{
Console.WriteLine("Downloading dataset");
var news20 = new Accord.DataSets.News20(@"C:\Temp\");
Sparse <double>[] inputs = news20.Training.Item1;
int[] outputs = news20.Training.Item2.ToMulticlass();
var learn = new MultilabelSupportVectorLearning <Linear, Sparse <double> >()
{
// using LIBLINEAR's L2-loss SVC dual for each SVM
Learner = (p) => new LinearDualCoordinateDescent <Linear, Sparse <double> >()
{
Loss = Loss.L2,
Complexity = 1.0,
Tolerance = 1e-4
}
};
Console.WriteLine("Learning");
Stopwatch sw = Stopwatch.StartNew();
var svm = learn.Learn(inputs.Get(0, 100), outputs.Get(0, 100));
Console.WriteLine(sw.Elapsed);
Console.WriteLine("Predicting");
sw = Stopwatch.StartNew();
int[] predicted = svm.ToMulticlass().Decide(inputs);
Console.WriteLine(sw.Elapsed);
}
private static void TestSparseSVMComplete()
{
#region doc_learn_news20
Console.WriteLine("Downloading dataset:");
var news20 = new Accord.DataSets.News20(@"C:\Temp\");
var trainInputs = news20.Training.Item1;
var trainOutputs = news20.Training.Item2.ToMulticlass();
var testInputs = news20.Testing.Item1;
var testOutputs = news20.Testing.Item2.ToMulticlass();
Console.WriteLine(" - Training samples: {0}", trainInputs.Rows());
Console.WriteLine(" - Testing samples: {0}", testInputs.Rows());
Console.WriteLine(" - Dimensions: {0}", trainInputs.Columns());
Console.WriteLine(" - Classes: {0}", trainOutputs.DistinctCount());
Console.WriteLine();
// Create and use the learning algorithm to train a sparse linear SVM
var learn = new MultilabelSupportVectorLearning <Linear, Sparse <double> >()
{
// using LIBLINEAR's L2-loss SVC dual for each SVM
Learner = (p) => new LinearDualCoordinateDescent <Linear, Sparse <double> >()
{
Loss = Loss.L2,
Tolerance = 1e-4
},
};
// Display progress in the console
learn.SubproblemFinished += (sender, e) =>
{
Console.WriteLine(" - {0} / {1} ({2:00.0%})", e.Progress, e.Maximum, e.Progress / (double)e.Maximum);
};
// Start the learning algorithm
Console.WriteLine("Learning");
Stopwatch sw = Stopwatch.StartNew();
var svm = learn.Learn(trainInputs, trainOutputs);
Console.WriteLine("Done in {0}", sw.Elapsed);
Console.WriteLine();
// Compute accuracy in the training set
Console.WriteLine("Predicting training set");
sw = Stopwatch.StartNew();
int[] trainPredicted = svm.ToMulticlass().Decide(trainInputs);
Console.WriteLine("Done in {0}", sw.Elapsed);
double trainError = new ZeroOneLoss(trainOutputs).Loss(trainPredicted);
Console.WriteLine("Training error: {0}", trainError);
Console.WriteLine();
// Compute accuracy in the testing set
Console.WriteLine("Predicting testing set");
sw = Stopwatch.StartNew();
int[] testPredicted = svm.ToMulticlass().Decide(testInputs);
Console.WriteLine("Done in {0}", sw.Elapsed);
double testError = new ZeroOneLoss(testOutputs).Loss(testPredicted);
Console.WriteLine("Testing error: {0}", testError);
#endregion
}