public void GuessDimensionsInMiddleRunTest()
{
MemoryStream file = new MemoryStream(
Encoding.Default.GetBytes(Resources.a9a_train));
SparseReader reader = new SparseReader(file, Encoding.Default);
var r = reader.ReadDenseToEnd();
double[][] samples = r.Item1;
int[] labels = r.Item2.ToInt32();
string[] descriptions = reader.SampleDescriptions.ToArray();
Assert.AreEqual(26049, samples.Length);
for (int i = 0; i < labels.Length; i++)
{
Assert.IsTrue(labels[i] == -1 || labels[i] == 1);
Assert.IsTrue(descriptions[i] == String.Empty);
Assert.AreEqual(123, samples[i].Length);
}
}
public void GuessNumberOfDimensionsTest()
{
MemoryStream file = new MemoryStream(
Encoding.Default.GetBytes(Resources.iris_scale));
SparseReader reader = new SparseReader(file, Encoding.Default);
Assert.AreEqual(4, reader.Dimensions);
var r = reader.ReadDenseToEnd();
double[][] samples = r.Item1;
int[] labels = r.Item2.ToInt32();
string[] descriptions = reader.SampleDescriptions.ToArray();
Assert.AreEqual(150, samples.Length);
for (int i = 0; i < 150; i++)
{
Assert.IsTrue(labels[i] >= 0 && labels[i] <= 3);
Assert.IsTrue(descriptions[i] == String.Empty);
Assert.AreEqual(4, samples[i].Length);
}
}
public void SparseLinearTest()
{
MulticlassSupportVectorMachine<Linear> svm1;
MulticlassSupportVectorMachine<Linear, Sparse<double>> svm2;
{
Accord.Math.Random.Generator.Seed = 0;
MemoryStream file = new MemoryStream(
Encoding.Default.GetBytes(Resources.iris_scale));
var reader = new SparseReader(file, Encoding.Default);
var samples = reader.ReadDenseToEnd();
double[][] x = samples.Item1;
int[] y = samples.Item2.ToMulticlass();
var learner = new MulticlassSupportVectorLearning<Linear>()
{
Learner = (p) => new LinearDualCoordinateDescent<Linear>()
};
svm1 = learner.Learn(x, y);
}
{
Accord.Math.Random.Generator.Seed = 0;
MemoryStream file = new MemoryStream(
Encoding.Default.GetBytes(Resources.iris_scale));
// Create a new Sparse Sample Reader to read any given file,
// passing the correct dense sample size in the constructor
var reader = new SparseReader(file, Encoding.Default);
var samples = reader.ReadSparseToEnd();
Sparse<double>[] x = samples.Item1;
int[] y = samples.Item2.ToMulticlass();
var learner = new MulticlassSupportVectorLearning<Linear, Sparse<double>>()
{
Learner = (p) => new LinearDualCoordinateDescent<Linear, Sparse<double>>()
};
svm2 = learner.Learn(x, y);
}
Assert.AreEqual(svm1.Models.Length, svm2.Models.Length);
for (int i = 0; i < svm1.Models.Length; i++)
{
var ma = svm1[i].Value;
var mb = svm2[i].Value;
Assert.IsTrue(ma.Weights.IsEqual(mb.Weights));
Assert.AreEqual(ma.SupportVectors.Length, mb.SupportVectors.Length);
for (int j = 0; j < ma.SupportVectors.Length; j++)
{
double[] expected = ma.SupportVectors[j];
double[] actual = mb.SupportVectors[j].ToDense(4);
Assert.IsTrue(expected.IsEqual(actual, 1e-5));
}
}
}
public void ReadAllTest()
{
MemoryStream file = new MemoryStream(
Encoding.Default.GetBytes(Resources.iris_scale));
// Suppose we are going to read a sparse sample file containing
// samples which have an actual dimension of 4. Since the samples
// are in a sparse format, each entry in the file will probably
// have a much lesser number of elements.
int sampleSize = 4;
// Create a new Sparse Sample Reader to read any given file,
// passing the correct dense sample size in the constructor
SparseReader reader = new SparseReader(file, Encoding.Default, sampleSize);
// Declare a vector to obtain the label
// of each of the samples in the file
// Declare a vector to obtain the description (or comments)
// about each of the samples in the file, if present.
// Read the sparse samples and store them in a dense vector array
var r = reader.ReadDenseToEnd();
double[][] samples = r.Item1;
int[] labels = r.Item2.ToInt32();
string[] descriptions = reader.SampleDescriptions.ToArray();
Assert.AreEqual(150, samples.Length);
for (int i = 0; i < 150; i++)
{
Assert.IsTrue(labels[i] >= 0 && labels[i] <= 3);
Assert.IsTrue(descriptions[i] == String.Empty);
Assert.AreEqual(4, samples[i].Length);
}
}
/// <summary>
/// Reads a problem specified in LibSVM's sparse format.
/// </summary>
///
public static Problem read_problem(string filename, double bias)
{
// Create a LibSVM's sparse data reader
var reader = new SparseReader(filename);
if (bias > 0)
reader.Intercept = bias;
var r = reader.ReadDenseToEnd();
double[][] x = r.Item1;
double[] y = r.Item2;
return new Problem()
{
Dimensions = reader.Dimensions,
Inputs = x,
Outputs = y,
};
}
