コード例 #1
0
ファイル: eigen_embedding.hpp プロジェクト: AlexBinder/shogun
	EmbeddingResult embed(const MatrixType& wm, IndexType target_dimension, unsigned int skip)
	{
		timed_context context("Randomized eigendecomposition");
		
		DenseMatrix O(wm.rows(), target_dimension+skip);
		for (IndexType i=0; i<O.rows(); ++i)
		{
			IndexType j=0;
			for ( ; j+1 < O.cols(); j+= 2)
			{
				ScalarType v1 = (ScalarType)(rand()+1.f)/((float)RAND_MAX+2.f);
				ScalarType v2 = (ScalarType)(rand()+1.f)/((float)RAND_MAX+2.f);
				ScalarType len = sqrt(-2.f*log(v1));
				O(i,j) = len*cos(2.f*M_PI*v2);
				O(i,j+1) = len*sin(2.f*M_PI*v2);
			}
			for ( ; j < O.cols(); j++)
			{
				ScalarType v1 = (ScalarType)(rand()+1.f)/((float)RAND_MAX+2.f);
				ScalarType v2 = (ScalarType)(rand()+1.f)/((float)RAND_MAX+2.f);
				ScalarType len = sqrt(-2.f*log(v1));
				O(i,j) = len*cos(2.f*M_PI*v2);
			}
		}
		MatrixTypeOperation operation(wm);

		DenseMatrix Y = operation(O);
		for (IndexType i=0; i<Y.cols(); i++)
		{
			for (IndexType j=0; j<i; j++)
			{
				ScalarType r = Y.col(i).dot(Y.col(j));
				Y.col(i) -= r*Y.col(j);
			}
			ScalarType norm = Y.col(i).norm();
			if (norm < 1e-4)
			{
				for (int k = i; k<Y.cols(); k++)
					Y.col(k).setZero();
			}
			Y.col(i) *= (1.f / norm);
		}

		DenseMatrix B1 = operation(Y);
		DenseMatrix B = Y.householderQr().solve(B1);
		DenseSelfAdjointEigenSolver eigenOfB(B);

		if (eigenOfB.info() == Eigen::Success)
		{
			DenseMatrix embedding = (Y*eigenOfB.eigenvectors()).block(0, skip, wm.cols(), target_dimension);
			return EmbeddingResult(embedding,eigenOfB.eigenvalues());
		}
		else
		{
			throw eigendecomposition_error("eigendecomposition failed");
		}
		return EmbeddingResult();
	}
コード例 #2
0
ファイル: eigendecomposition.hpp プロジェクト: hushell/shogun
EigendecompositionResult eigendecomposition_impl_randomized(const MatrixType& wm, IndexType target_dimension, unsigned int skip)
{
	timed_context context("Randomized eigendecomposition");
	
	DenseMatrix O(wm.rows(), target_dimension+skip);
	for (IndexType i=0; i<O.rows(); ++i)
	{
		for (IndexType j=0; j<O.cols(); j++)
		{
			O(i,j) = tapkee::gaussian_random();
		}
	}
	MatrixOperationType operation(wm);

	DenseMatrix Y = operation(O);
	for (IndexType i=0; i<Y.cols(); i++)
	{
		for (IndexType j=0; j<i; j++)
		{
			ScalarType r = Y.col(i).dot(Y.col(j));
			Y.col(i) -= r*Y.col(j);
		}
		ScalarType norm = Y.col(i).norm();
		if (norm < 1e-4)
		{
			for (int k = i; k<Y.cols(); k++)
				Y.col(k).setZero();
		}
		Y.col(i) *= (1.f / norm);
	}

	DenseMatrix B1 = operation(Y);
	DenseMatrix B = Y.householderQr().solve(B1);
	DenseSelfAdjointEigenSolver eigenOfB(B);

	if (eigenOfB.info() == Eigen::Success)
	{
		if (MatrixOperationType::largest)
		{
			assert(skip==0);
			DenseMatrix selected_eigenvectors = (Y*eigenOfB.eigenvectors()).rightCols(target_dimension);
			return EigendecompositionResult(selected_eigenvectors,eigenOfB.eigenvalues());
		} 
		else
		{
			DenseMatrix selected_eigenvectors = (Y*eigenOfB.eigenvectors()).leftCols(target_dimension+skip).rightCols(target_dimension);
			return EigendecompositionResult(selected_eigenvectors,eigenOfB.eigenvalues());
		}
	}
	else
	{
		throw eigendecomposition_error("eigendecomposition failed");
	}
	return EigendecompositionResult();
}