public void ReadRowsInSecondScanDoNotUseOriginalReaderForEmptyRowsArray()
{
// arrange
const int rows = 0;
const int columns = 0;
const double density = 0.01;
var originalVectors = SparseVectorHelper.GenerateSparseVectors(rows, columns, density, () => SparseVectorHelper.RandomInInterval(-100, 100, 2)).ToArray();
var originalReader = new InMemorySparseMatrixReader(originalVectors);
var originalReaderWithCounters = new SparseMatrixReaderWithMemberInvocationCounters <double>(originalReader);
using (var cachedReader = new CachedMatrixMarketReader <double>(originalReaderWithCounters))
{
// act
foreach (var row in cachedReader.ReadRows())
{
}
foreach (var row in cachedReader.ReadRows())
{
}
// assert
Assert.AreEqual(1, originalReaderWithCounters.ColumnsCountInvocations);
Assert.AreEqual(1, originalReaderWithCounters.RowsCountInvocations);
Assert.AreEqual(1, originalReaderWithCounters.ElementsCountInvocations);
Assert.AreEqual(1, originalReaderWithCounters.ReadRowsInvocations);
}
}
public void ConstructorThrowsArgumentNullExceptionForNullReaderArgument()
{
// arrange
// act
using (var cachedReader = new CachedMatrixMarketReader <double>(null))
{
}
// assert
Assert.Fail();
}
public void InitializedCachedMatrixMarketReaderReturnsTheSamePropertiesValuesForEmptyRowsArray()
{
// arrange
var reader = new InMemorySparseMatrixReader();
using (var cachedReader = new CachedMatrixMarketReader <double>(reader))
{
// act
// assert
Assert.AreEqual(reader.ColumnsCount, cachedReader.ColumnsCount);
Assert.AreEqual(reader.RowsCount, cachedReader.RowsCount);
Assert.AreEqual(reader.ElementsCount, cachedReader.ElementsCount);
}
}
public void ReadRowsReturnSameVectorsInFirstScanForEmptyRowsArray()
{
// arrange
const int rows = 0;
const int columns = 0;
const double density = 0.01;
var originalVectors = SparseVectorHelper.GenerateSparseVectors(rows, columns, density, () => SparseVectorHelper.RandomInInterval(-100, 100, 2)).ToArray();
var originalReader = new InMemorySparseMatrixReader(originalVectors);
using (var cachedReader = new CachedMatrixMarketReader <double>(originalReader))
{
// act
var firstScanResults = originalReader.ReadRows().ZipFull(cachedReader.ReadRows(), (originalVector, cachedVector) => originalVector == cachedVector).All(b => b);
// assert
Assert.IsTrue(firstScanResults);
}
}
static void Main(string[] args)
{
const string matrixFileName = @"c:\temp\matrix.txt";
const int rows = 10000;
const int columns = 1000;
const double density = 0.01;
const int featuresCount = 10;
const int iterationsCount = 30;
Console.WriteLine("Generating matrix {0}x{1} with avg. {2} non-zero elements ({3}%)...", rows, columns, (long)((double)rows * columns * density), density * 100.0);
using (var file = new FileStream(matrixFileName, FileMode.Create))
using (var mmwriter = new MatrixMarketWriter <double>(file))
{
var vectors = SparseVectorHelper.GenerateSparseVectors(rows, columns, density, () => SparseVectorHelper.RandomInInterval(0.01, 100, 2));
mmwriter.Write(vectors);
}
Console.WriteLine("Matrix factorization (features=" + featuresCount + ", iterations=" + iterationsCount + ")...");
using (var file = new FileStream(matrixFileName, FileMode.Open))
using (var mmreader = new MatrixMarketReader <double>(file))
using (var cachedReader = new CachedMatrixMarketReader <double>(mmreader))
{
var nmf = new NMF(cachedReader);
// act
var sw = Stopwatch.StartNew();
using (var factorization = nmf.Factorize(featuresCount, iterationsCount))
{
sw.Stop();
Console.WriteLine("Factorization time: " + sw.ElapsedMilliseconds + " ms.");
Console.WriteLine("Euclidean distance calculation...");
sw = Stopwatch.StartNew();
var euclideanDistance = nmf.GetEuclideanDistance(factorization);
sw.Stop();
Console.WriteLine("Euclidean distance calculation time: " + sw.ElapsedMilliseconds + " ms.");
Console.WriteLine("Factorization Euclidean distance: " + euclideanDistance);
}
}
}
static void Main(string[] args)
{
const string matrixFileName = @"c:\temp\matrix.txt";
const int rows = 10000;
const int columns = 1000;
const double density = 0.01;
const int featuresCount = 10;
const int iterationsCount = 30;
Console.WriteLine("Generating matrix {0}x{1} with avg. {2} non-zero elements ({3}%)...", rows, columns, (long)((double)rows * columns * density), density * 100.0);
using (var file = new FileStream(matrixFileName, FileMode.Create))
using (var mmwriter = new MatrixMarketWriter<double>(file))
{
var vectors = SparseVectorHelper.GenerateSparseVectors(rows, columns, density, () => SparseVectorHelper.RandomInInterval(0.01, 100, 2));
mmwriter.Write(vectors);
}
Console.WriteLine("Matrix factorization (features=" + featuresCount + ", iterations=" + iterationsCount + ")...");
using (var file = new FileStream(matrixFileName, FileMode.Open))
using (var mmreader = new MatrixMarketReader<double>(file))
using (var cachedReader = new CachedMatrixMarketReader<double>(mmreader))
{
var nmf = new NMF(cachedReader);
// act
var sw = Stopwatch.StartNew();
using (var factorization = nmf.Factorize(featuresCount, iterationsCount))
{
sw.Stop();
Console.WriteLine("Factorization time: " + sw.ElapsedMilliseconds + " ms.");
Console.WriteLine("Euclidean distance calculation...");
sw = Stopwatch.StartNew();
var euclideanDistance = nmf.GetEuclideanDistance(factorization);
sw.Stop();
Console.WriteLine("Euclidean distance calculation time: " + sw.ElapsedMilliseconds + " ms.");
Console.WriteLine("Factorization Euclidean distance: " + euclideanDistance);
}
}
}
public void InitializedCachedMatrixMarketReaderReturnsTheSamePropertiesValues()
{
// arrange
var reader = new InMemorySparseMatrixReader(
new SparseVector <double> {
{ 0, 22.0 }, { 1, 28.0 }
},
new SparseVector <double> {
{ 0, 49.0 }, { 1, 64.0 }
});
using (var cachedReader = new CachedMatrixMarketReader <double>(reader))
{
// act
// assert
Assert.AreEqual(reader.ColumnsCount, cachedReader.ColumnsCount);
Assert.AreEqual(reader.RowsCount, cachedReader.RowsCount);
Assert.AreEqual(reader.ElementsCount, cachedReader.ElementsCount);
}
}